IODS course project
- Introduction to Open Data Science - Course Project
- About the project
- Lets explore the distributions and relationships of the variables
- Excercise 2, the PCA
- Lets do the same for scaled variables
- Give your personal interpretations of the first two principal component dimensions based on the biplot drawn after PCA on the standardized human data. (0-2 points)
- Load the tea dataset from the package Factominer. Explore the data briefly: look at the structure and the dimensions of the data and visualize it. Then do Multiple Correspondence Analysis on the tea data (or to a certain columns of the data, it’s up to you). Interpret the results of the MCA and draw at least the variable biplot of the analysis. You can also explore other plotting options for MCA. Comment on the output of the plots
Introduction to Open Data Science - Course Project
About the project
kissa iso
# This is a so-called "R chunk" where you can write R code.
date()## [1] "Sun Dec 05 12:38:39 2021"Well, I have no clue what I think about this course. I have had a great trouble with sisu, and therefore I was suprised that my signup was accepted. However, lets see how this goes. linkkiIODS Tein hyperlinkin perille koska testi. Tässä peruslinkki https://github.com/AdaPe/IODS-project.git kissa
#Excercises for week 2
setwd(“~/IODS-project”)
I have done some linear models. I also lost my github button, but luckily I got it back! And obviously this likes to happen on times such as sunday night!Hmm, lets see what happens, now the github diary does not work
date()## [1] "Sun Dec 05 12:38:39 2021"Here we go again. This next session lists all the libraries needed for completing this excercise:
library("ggplot2")
library("GGally")## Warning: package 'GGally' was built under R version 3.6.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2Lets start with reading the dataframe from the provided link. We shall also look at the data more closely by using the summary function. I am not printing here the structure of our data, because from the environment page it can be seen.
data<- read.table(url("http://s3.amazonaws.com/assets.datacamp.com/production/course_2218/datasets/learning2014.txt"),
sep = ",", header = T)
summary(data)## gender age attitude deep stra
## F:110 Min. :17.00 Min. :1.400 Min. :1.583 Min. :1.250
## M: 56 1st Qu.:21.00 1st Qu.:2.600 1st Qu.:3.333 1st Qu.:2.625
## Median :22.00 Median :3.200 Median :3.667 Median :3.188
## Mean :25.51 Mean :3.143 Mean :3.680 Mean :3.121
## 3rd Qu.:27.00 3rd Qu.:3.700 3rd Qu.:4.083 3rd Qu.:3.625
## Max. :55.00 Max. :5.000 Max. :4.917 Max. :5.000
## surf points
## Min. :1.583 Min. : 7.00
## 1st Qu.:2.417 1st Qu.:19.00
## Median :2.833 Median :23.00
## Mean :2.787 Mean :22.72
## 3rd Qu.:3.167 3rd Qu.:27.75
## Max. :4.333 Max. :33.00This is a classic dataframe with 166 rows and 7 columns: gender, age, attitude, deep, stra, surf and points. It seems that this dataframe is the same we created with data wrangling excercise. However, I think each row is an individual and columns are results from that individual.
There are 110 females and 56 males in the data. Mean age is 25.51 years. Variables attitude means global attitude towards statistics, points are derived from the exam and variables deep, stra, and surf were constructed from subquestions as in previous excercise.
From now on, abbreviations will be used, but different variables mean these things:
stra = Strategic approach deep = deep approach surf = surface approach
ggpairs(data, mapping = aes(alpha = 0.3, col =gender))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(data)## gender age attitude deep stra
## F:110 Min. :17.00 Min. :1.400 Min. :1.583 Min. :1.250
## M: 56 1st Qu.:21.00 1st Qu.:2.600 1st Qu.:3.333 1st Qu.:2.625
## Median :22.00 Median :3.200 Median :3.667 Median :3.188
## Mean :25.51 Mean :3.143 Mean :3.680 Mean :3.121
## 3rd Qu.:27.00 3rd Qu.:3.700 3rd Qu.:4.083 3rd Qu.:3.625
## Max. :55.00 Max. :5.000 Max. :4.917 Max. :5.000
## surf points
## Min. :1.583 Min. : 7.00
## 1st Qu.:2.417 1st Qu.:19.00
## Median :2.833 Median :23.00
## Mean :2.787 Mean :22.72
## 3rd Qu.:3.167 3rd Qu.:27.75
## Max. :4.333 Max. :33.00From the graphical oveview of the data, we can see that we have more women than menas a test subjects. The average age of women is smaller than men.
Men have generally a bit better attitude towards statistics, however, it is not stated as statistically significant. I don’t see other differences between gender and individual variables (boxplots are quite overlapping).
It seems that the age is not normally distributed variable, the peak is in on the right side of the graph. Thus, people included into this study seem to be generally young rather than old.
When it comes to attitude, it seems that attitudes of women is almost normally distributed while men have a bit left tilted graph. (still, this might be okay to say its normally distributed).
Deep seems to also be a bit leaning to left as a graph, it would be good to chech the distribution with some calculation method rather than looking the graphs.
Stra and surf learnings seems to be normally distributed variables, while the points is not (it has this odd tail on the right side).
It seems that there is startistically significant correlation between the attitude towards statistics and points obtained from the exam. This is true for both genders.
Also surface and deep learning seem to be strongly negatively correlated in the population including both genders and among men.
Attitude and surface learning also seem to be negatively correlated, in the whole population and among men.
Surface and strategic learning seem to be correlated in whole population, but such correlation is not seen in one-gender-only populations.
For our regression model we ought to choose (from the graphical output, three variables that have the best correlation with points).
Three having the greatest correlation values with points according to our graphical interpretation seem to be attitude (0.437, positively correlated and statistically significant), stra (0.146, not statistically significant), and surf (-0.144, so negatively correlated, not statistically significant).
linearmode<- lm(points ~ attitude + stra + surf, data = data)
summary(linearmode)##
## Call:
## lm(formula = points ~ attitude + stra + surf, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.1550 -3.4346 0.5156 3.6401 10.8952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.0171 3.6837 2.991 0.00322 **
## attitude 3.3952 0.5741 5.913 1.93e-08 ***
## stra 0.8531 0.5416 1.575 0.11716
## surf -0.5861 0.8014 -0.731 0.46563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.296 on 162 degrees of freedom
## Multiple R-squared: 0.2074, Adjusted R-squared: 0.1927
## F-statistic: 14.13 on 3 and 162 DF, p-value: 3.156e-08From the summary of linear model we can observe following: There is a significant correlation between attitude and points. Also, the crossing of the axis is not 0, which is also a significant observation. Other variables in this model are not significantand thus they are left out from the model.
Lets make new model with attitude, gender and deep.
linearmode<- lm(points ~ attitude + gender + deep, data = data)
summary(linearmode)##
## Call:
## lm(formula = points ~ attitude + gender + deep, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.0364 -3.2315 0.3561 3.9436 11.0859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.6240 3.1799 4.284 3.13e-05 ***
## attitude 3.6657 0.5984 6.125 6.61e-09 ***
## genderM -0.4633 0.9170 -0.505 0.614
## deep -0.6172 0.7546 -0.818 0.415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.337 on 162 degrees of freedom
## Multiple R-squared: 0.1953, Adjusted R-squared: 0.1804
## F-statistic: 13.1 on 3 and 162 DF, p-value: 1.054e-07Again, the only statsistically significant correlation is between attitude and points. For us students this is a happy finding, a better attitude we have, better results we will have:)
So my final linear model is following:
linearmode<- lm(points ~ attitude, data = data)
summary(linearmode)##
## Call:
## lm(formula = points ~ attitude, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.9763 -3.2119 0.4339 4.1534 10.6645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.6372 1.8303 6.358 1.95e-09 ***
## attitude 3.5255 0.5674 6.214 4.12e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.32 on 164 degrees of freedom
## Multiple R-squared: 0.1906, Adjusted R-squared: 0.1856
## F-statistic: 38.61 on 1 and 164 DF, p-value: 4.119e-09The residuals in my model are reflecting how well my model predicted the actual value of y. Thus, these are the difference between actual points value and calculated points value. If residuals are symmetrically divided to either side of my plot, the value is 0. Because the median is over 0, there are more samples above the model thatn under the model. However, there are samples that my model predicts too high values especially in the lower scale (residual minimal is -16).
Coefficients have the estimated value, std error from the residuals, t-value (estimate divided by standard error) and Pr(>|t|) which is the lookup of the t-value in t-distribution table with given degrees of freedom.
In the bottom of the summary output we have the residual standard error, which is very similar for standard deviation. Thus, reflecting how much there is varition in errors.
Multiple R-squared represents how well my model is reflecting my data. It is calculated by counting the explained variation of the model by the total variation of the model. If my model predicts things well, we have R-squared close to 1, while poorly performing model might even have negative values My model predicts now 19.06 percent of the whole variation. According to the datacamp, R-squared over 0.5 is good, so the model is not very accurate.
If we raise it to the second power, we will really see how well my model is reflecting my data. 3.632836 % can be seen to be caused by attitude variable.
Adjusted R-squared is multiple r-squared adjusted for the multiple hypothesis testing (if we would have several variables). Because the R-value tends to get bigger even though there would be unsignificant variables.
F-statistic: This parameter is quite like a t-test for the whole model, it gives me a p-value about how likely my model is to be just randomly fitting my data like this.
par(mfrow = c(2,2))
plot(linearmode)
Here we are able to see, that our data contains some outliers (the residuals vs fitted-line is not straight and the outliers are indicated in the plot).
Homoscedasticity means that one variable has approximately similar variability in all values of other variable. We can see from Q_Q plot that our model does not differ significantly from the predicted residuals presented in optimal theorethical model. I think these observations are almost perfectly in line with theorethically predicted residuals.
Heteroskedasticity means that the variability of dependent variable is altering significantly if the value of explaining variable is altered.
For observunf thse, we can look at the scale location plot where we want to really see two things: 1. line is horisontal 2. The value spread around red lines is not variating as much as fitted values (seems ok!).
And the last graph, residuals vs leverage is describing how close or far the points are from each other. We have some points with a bit higher leverage, but none of the is outside from Cook’s distance and thus deleting them might not have significant influence on our model.
#Week 3 excercises #Aino Peura Libraries used for this R-markdown file
library(readr)## Warning: package 'readr' was built under R version 3.6.3library(ggplot2)
library(GGally)
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(boot)#About the data
So, lets begin the week 3 excercises. In this excercise we will be using data from “Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez”. It describes achievements and sosioeconomial variables of portuguese students. The data was collected with reports and questionnaires.
In the joined data, m means math and p means portuguese. The list of variables and explanations are following. For some reason, in our joined data some m and p variables from data combination were left in our data, even though it is meaningless, because for example absences are identical. However, in this excercise, we will use the official joined data.
This list is copied from the website: https://archive.ics.uci.edu/ml/datasets/Student+Performance, and it lacks folloring attributes: alc_use (combination of week and weekend alcohol use), high_use = if alcohol use is >2.
1 school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira)
2 sex - student’s sex (binary: ‘F’ - female or ‘M’ - male)
3 age - student’s age (numeric: from 15 to 22)
4 address - student’s home address type (binary: ‘U’ - urban or ‘R’ - rural)
5 famsize - family size (binary: ‘LE3’ - less or equal to 3 or ‘GT3’ - greater than 3)
6 Pstatus - parent’s cohabitation status (binary: ‘T’ - living together or ‘A’ - apart)
7 Medu - mother’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 5th to 9th grade, 3 secondary education or 4 higher education)
8 Fedu - father’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 5th to 9th grade, 3 secondary education or 4 higher education)
9 Mjob - mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
10 Fjob - father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
11 reason - reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’)
12 guardian - student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’)
13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16 schoolsup - extra educational support (binary: yes or no)
17 famsup - family educational support (binary: yes or no)
18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19 activities - extra-curricular activities (binary: yes or no)
20 nursery - attended nursery school (binary: yes or no)
21 higher - wants to take higher education (binary: yes or no)
22 internet - Internet access at home (binary: yes or no)
23 romantic - with a romantic relationship (binary: yes or no)
24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29 health - current health status (numeric: from 1 - very bad to 5 - very good)
30 absences - number of school absences (numeric: from 0 to 93)
these grades are obtained individually for each course subject, math or portuquese: 31 G1 - first period grade (numeric: from 0 to 20)
31 G2 - second period grade (numeric: from 0 to 20)
32 G3 - final grade (numeric: from 0 to 20, output target)
#1.Lets read the data:
joined<- read.csv(url("https://github.com/rsund/IODS-project/raw/master/data/alc.csv"))
colnames(joined)## [1] "school" "sex" "age" "address" "famsize"
## [6] "Pstatus" "Medu" "Fedu" "Mjob" "Fjob"
## [11] "reason" "guardian" "traveltime" "studytime" "schoolsup"
## [16] "famsup" "activities" "nursery" "higher" "internet"
## [21] "romantic" "famrel" "freetime" "goout" "Dalc"
## [26] "Walc" "health" "n" "id.p" "id.m"
## [31] "failures" "paid" "absences" "G1" "G2"
## [36] "G3" "failures.p" "paid.p" "absences.p" "G1.p"
## [41] "G2.p" "G3.p" "failures.m" "paid.m" "absences.m"
## [46] "G1.m" "G2.m" "G3.m" "alc_use" "high_use"
## [51] "cid"I think I will choose following variables for the glm model: famrel = quality of family relationships -> Because I think bad relationships lead to alcohol consumption health = I don’t think which way this will be, but at least I am interested? activities = if you have ec-activities, you have no time to drink absences = I have no clue why in this data this variable left this as absences p and m, because they are identical?. However, I will think if you have hangover, you don’t attend school-> thus more absences.
identical(joined$absences.m, joined$absences.m)## [1] TRUE#2. Lets make a subtable of our chosen variables
Lets explore our chosen variables and lets make a subtable of those. The name of the table will be joinedS, S significating as subtable.
joinedS<- joined[,c("high_use", "alc_use", "famrel", "health", "activities", "absences")]
ggpairs(joinedS, aes(col=high_use))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Allright, lets look at the variables more closely:
out of 370 students, a bit over 200 are low users and almost 100 are high users. The variable alcohol use seems to be normally distributed in high users group, in low users not. Variable “family relationships” seems to correlate weakly and significantly to alcohol use. Health seems not to correlate alcohol use and is not normally distributed (which is natural because most of us are really healthy). Activities don’t have the correlation either for the alcohol use. Hoever, absences seem to correlate stongly to alcohol usage.
Thus, my hypothesis was correct only with absences column and with family relationships .
#3. GLM:
Lets then create glm by using glm function:
model<- glm(high_use ~famrel+health+activities+absences, data=joinedS, family = "binomial")
summary(model)##
## Call:
## glm(formula = high_use ~ famrel + health + activities + absences,
## family = "binomial", data = joinedS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4366 -0.8130 -0.6946 1.1921 1.9699
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.59822 0.58265 -1.027 0.304550
## famrel -0.28173 0.12732 -2.213 0.026911 *
## health 0.15755 0.08736 1.803 0.071318 .
## activitiesyes -0.26473 0.23616 -1.121 0.262284
## absences 0.08563 0.02267 3.777 0.000159 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.04 on 369 degrees of freedom
## Residual deviance: 425.68 on 365 degrees of freedom
## AIC: 435.68
##
## Number of Fisher Scoring iterations: 4From the summary of our model, we can see that (as previously stated), the only significantly correlating variables are famrel and absences.
From the summary we can also obtain some values that tell us how well our model is working. However, our deviance is quite high, so we have a very poor model.
We can zoom into individual values in our model, but more reasonable is to look the quality of our predictions. Lets define coefficients and their confidence intervals: OR = odds ratio CI = Confidence interval
OR<- coef(model)%>% exp
CI<- exp(confint(model))## Waiting for profiling to be done...cbind(OR, CI)## OR 2.5 % 97.5 %
## (Intercept) 0.5497899 0.1724871 1.7087559
## famrel 0.7544778 0.5867116 0.9683109
## health 1.1706390 0.9888619 1.3938798
## activitiesyes 0.7674106 0.4820699 1.2185834
## absences 1.0894002 1.0441755 1.1415837Lets then create model only with family relationships and absences, because they were only significant ones:
In the following code probabilities and prediction variables that will show us how good our model is: And lets do some cross-tabulation:
model2<- glm(high_use ~famrel+absences, data=joinedS, family = "binomial")
joinedS$probabilities<- predict(model2, type = "response")
joinedS$Prediction<- ifelse(joinedS$probabilities>0.5, "High", "low")
table(joinedS$high_use, joinedS$Prediction)##
## High low
## FALSE 8 251
## TRUE 15 96table(joinedS$famrel, joinedS$Prediction)##
## High low
## 1 4 4
## 2 5 13
## 3 4 60
## 4 8 172
## 5 2 98Okay, then we will plot the values:
ggplot(joinedS, aes(high_use, probabilities, col=Prediction ))+
geom_point()+
theme_dark()
And now, I did not check how this was done in datacamp, but lets count how many points were missclassified (the training error) as percentage of false positive and false negative points:
Falsepos<- subset(joinedS, high_use == F & Prediction=="High")
Falseneg<- subset(joinedS, high_use == T & Prediction!="High")
missclass<- rbind.data.frame(Falseneg, Falsepos)
nrow(missclass)/nrow(joinedS)## [1] 0.2810811Well, approximately 1/3 of the values were missquessed -> This is better than simple quessing, because if we have to quess 370 times and only 104 times uncorrect, it is very unlikely that we obtain this good values.
#Bonus excercise 1, 10 times cross-validation
So, in the cross-validation we will test how well our data will perform. This task will be achieved with following steps:
- Defining the loss-function that counts the average predicting error (how many samples are in the wrong class/all samples.
losses<- function(class, prob){
n_wrong<-abs(class-prob)>0.5
mean(n_wrong)
}
losses(class = joinedS$high_use, prob = joinedS$probabilities)## [1] 0.2810811So my own method of counting falseful values was as correct as the method from data-camp (we obtained the same results.) Then, lets do the cross-validation step:
crossV<- cv.glm(data=joinedS, cost = losses, glmfit = model2, K =10)
crossV$delta## [1] 0.3000000 0.2956757Well, my model definitely has some higher error 0.29>0.26, in both, training and testing data. The right number is adjusted and thus slightly better. This difference between me and the data-camp is because we have different set of variables. In the datacamp model m, they use failures + absences + sex as variables. I use only absences and family relationship quality. This is why my model looks different. So yes, I can find such a model.
Aino Peura 28.11.2021 These are week 4 excercises.
Libraries used:
#install.packages("MASS")
#install.packages("corrplot")
#install.packages("Hmisc")
#install.packages("GGally")
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(corrplot)## Warning: package 'corrplot' was built under R version 3.6.3## corrplot 0.84 loadedlibrary(gridExtra)## Warning: package 'gridExtra' was built under R version 3.6.3##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
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## combinelibrary(dplyr)
library(Hmisc)## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:boot':
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## melanoma## Loading required package: survival## Warning: package 'survival' was built under R version 3.6.3##
## Attaching package: 'survival'## The following object is masked from 'package:boot':
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## aml## Loading required package: Formula## Warning: package 'Formula' was built under R version 3.6.3##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitslibrary(GGally)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:Hmisc':
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## subplot## The following object is masked from 'package:MASS':
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## select## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutLets load and explore the boston-dataset:
Boston## crim zn indus chas nox rm age dis rad tax ptratio black
## 1 0.00632 18.0 2.31 0 0.5380 6.575 65.2 4.0900 1 296 15.3 396.90
## 2 0.02731 0.0 7.07 0 0.4690 6.421 78.9 4.9671 2 242 17.8 396.90
## 3 0.02729 0.0 7.07 0 0.4690 7.185 61.1 4.9671 2 242 17.8 392.83
## 4 0.03237 0.0 2.18 0 0.4580 6.998 45.8 6.0622 3 222 18.7 394.63
## 5 0.06905 0.0 2.18 0 0.4580 7.147 54.2 6.0622 3 222 18.7 396.90
## 6 0.02985 0.0 2.18 0 0.4580 6.430 58.7 6.0622 3 222 18.7 394.12
## 7 0.08829 12.5 7.87 0 0.5240 6.012 66.6 5.5605 5 311 15.2 395.60
## 8 0.14455 12.5 7.87 0 0.5240 6.172 96.1 5.9505 5 311 15.2 396.90
## 9 0.21124 12.5 7.87 0 0.5240 5.631 100.0 6.0821 5 311 15.2 386.63
## 10 0.17004 12.5 7.87 0 0.5240 6.004 85.9 6.5921 5 311 15.2 386.71
## 11 0.22489 12.5 7.87 0 0.5240 6.377 94.3 6.3467 5 311 15.2 392.52
## 12 0.11747 12.5 7.87 0 0.5240 6.009 82.9 6.2267 5 311 15.2 396.90
## 13 0.09378 12.5 7.87 0 0.5240 5.889 39.0 5.4509 5 311 15.2 390.50
## 14 0.62976 0.0 8.14 0 0.5380 5.949 61.8 4.7075 4 307 21.0 396.90
## 15 0.63796 0.0 8.14 0 0.5380 6.096 84.5 4.4619 4 307 21.0 380.02
## 16 0.62739 0.0 8.14 0 0.5380 5.834 56.5 4.4986 4 307 21.0 395.62
## 17 1.05393 0.0 8.14 0 0.5380 5.935 29.3 4.4986 4 307 21.0 386.85
## 18 0.78420 0.0 8.14 0 0.5380 5.990 81.7 4.2579 4 307 21.0 386.75
## 19 0.80271 0.0 8.14 0 0.5380 5.456 36.6 3.7965 4 307 21.0 288.99
## 20 0.72580 0.0 8.14 0 0.5380 5.727 69.5 3.7965 4 307 21.0 390.95
## 21 1.25179 0.0 8.14 0 0.5380 5.570 98.1 3.7979 4 307 21.0 376.57
## 22 0.85204 0.0 8.14 0 0.5380 5.965 89.2 4.0123 4 307 21.0 392.53
## 23 1.23247 0.0 8.14 0 0.5380 6.142 91.7 3.9769 4 307 21.0 396.90
## 24 0.98843 0.0 8.14 0 0.5380 5.813 100.0 4.0952 4 307 21.0 394.54
## 25 0.75026 0.0 8.14 0 0.5380 5.924 94.1 4.3996 4 307 21.0 394.33
## 26 0.84054 0.0 8.14 0 0.5380 5.599 85.7 4.4546 4 307 21.0 303.42
## 27 0.67191 0.0 8.14 0 0.5380 5.813 90.3 4.6820 4 307 21.0 376.88
## 28 0.95577 0.0 8.14 0 0.5380 6.047 88.8 4.4534 4 307 21.0 306.38
## 29 0.77299 0.0 8.14 0 0.5380 6.495 94.4 4.4547 4 307 21.0 387.94
## 30 1.00245 0.0 8.14 0 0.5380 6.674 87.3 4.2390 4 307 21.0 380.23
## 31 1.13081 0.0 8.14 0 0.5380 5.713 94.1 4.2330 4 307 21.0 360.17
## 32 1.35472 0.0 8.14 0 0.5380 6.072 100.0 4.1750 4 307 21.0 376.73
## 33 1.38799 0.0 8.14 0 0.5380 5.950 82.0 3.9900 4 307 21.0 232.60
## 34 1.15172 0.0 8.14 0 0.5380 5.701 95.0 3.7872 4 307 21.0 358.77
## 35 1.61282 0.0 8.14 0 0.5380 6.096 96.9 3.7598 4 307 21.0 248.31
## 36 0.06417 0.0 5.96 0 0.4990 5.933 68.2 3.3603 5 279 19.2 396.90
## 37 0.09744 0.0 5.96 0 0.4990 5.841 61.4 3.3779 5 279 19.2 377.56
## 38 0.08014 0.0 5.96 0 0.4990 5.850 41.5 3.9342 5 279 19.2 396.90
## 39 0.17505 0.0 5.96 0 0.4990 5.966 30.2 3.8473 5 279 19.2 393.43
## 40 0.02763 75.0 2.95 0 0.4280 6.595 21.8 5.4011 3 252 18.3 395.63
## 41 0.03359 75.0 2.95 0 0.4280 7.024 15.8 5.4011 3 252 18.3 395.62
## 42 0.12744 0.0 6.91 0 0.4480 6.770 2.9 5.7209 3 233 17.9 385.41
## 43 0.14150 0.0 6.91 0 0.4480 6.169 6.6 5.7209 3 233 17.9 383.37
## 44 0.15936 0.0 6.91 0 0.4480 6.211 6.5 5.7209 3 233 17.9 394.46
## 45 0.12269 0.0 6.91 0 0.4480 6.069 40.0 5.7209 3 233 17.9 389.39
## 46 0.17142 0.0 6.91 0 0.4480 5.682 33.8 5.1004 3 233 17.9 396.90
## 47 0.18836 0.0 6.91 0 0.4480 5.786 33.3 5.1004 3 233 17.9 396.90
## 48 0.22927 0.0 6.91 0 0.4480 6.030 85.5 5.6894 3 233 17.9 392.74
## 49 0.25387 0.0 6.91 0 0.4480 5.399 95.3 5.8700 3 233 17.9 396.90
## 50 0.21977 0.0 6.91 0 0.4480 5.602 62.0 6.0877 3 233 17.9 396.90
## 51 0.08873 21.0 5.64 0 0.4390 5.963 45.7 6.8147 4 243 16.8 395.56
## 52 0.04337 21.0 5.64 0 0.4390 6.115 63.0 6.8147 4 243 16.8 393.97
## 53 0.05360 21.0 5.64 0 0.4390 6.511 21.1 6.8147 4 243 16.8 396.90
## 54 0.04981 21.0 5.64 0 0.4390 5.998 21.4 6.8147 4 243 16.8 396.90
## 55 0.01360 75.0 4.00 0 0.4100 5.888 47.6 7.3197 3 469 21.1 396.90
## 56 0.01311 90.0 1.22 0 0.4030 7.249 21.9 8.6966 5 226 17.9 395.93
## 57 0.02055 85.0 0.74 0 0.4100 6.383 35.7 9.1876 2 313 17.3 396.90
## 58 0.01432 100.0 1.32 0 0.4110 6.816 40.5 8.3248 5 256 15.1 392.90
## 59 0.15445 25.0 5.13 0 0.4530 6.145 29.2 7.8148 8 284 19.7 390.68
## 60 0.10328 25.0 5.13 0 0.4530 5.927 47.2 6.9320 8 284 19.7 396.90
## 61 0.14932 25.0 5.13 0 0.4530 5.741 66.2 7.2254 8 284 19.7 395.11
## 62 0.17171 25.0 5.13 0 0.4530 5.966 93.4 6.8185 8 284 19.7 378.08
## 63 0.11027 25.0 5.13 0 0.4530 6.456 67.8 7.2255 8 284 19.7 396.90
## 64 0.12650 25.0 5.13 0 0.4530 6.762 43.4 7.9809 8 284 19.7 395.58
## 65 0.01951 17.5 1.38 0 0.4161 7.104 59.5 9.2229 3 216 18.6 393.24
## 66 0.03584 80.0 3.37 0 0.3980 6.290 17.8 6.6115 4 337 16.1 396.90
## 67 0.04379 80.0 3.37 0 0.3980 5.787 31.1 6.6115 4 337 16.1 396.90
## 68 0.05789 12.5 6.07 0 0.4090 5.878 21.4 6.4980 4 345 18.9 396.21
## 69 0.13554 12.5 6.07 0 0.4090 5.594 36.8 6.4980 4 345 18.9 396.90
## 70 0.12816 12.5 6.07 0 0.4090 5.885 33.0 6.4980 4 345 18.9 396.90
## 71 0.08826 0.0 10.81 0 0.4130 6.417 6.6 5.2873 4 305 19.2 383.73
## 72 0.15876 0.0 10.81 0 0.4130 5.961 17.5 5.2873 4 305 19.2 376.94
## 73 0.09164 0.0 10.81 0 0.4130 6.065 7.8 5.2873 4 305 19.2 390.91
## 74 0.19539 0.0 10.81 0 0.4130 6.245 6.2 5.2873 4 305 19.2 377.17
## 75 0.07896 0.0 12.83 0 0.4370 6.273 6.0 4.2515 5 398 18.7 394.92
## 76 0.09512 0.0 12.83 0 0.4370 6.286 45.0 4.5026 5 398 18.7 383.23
## 77 0.10153 0.0 12.83 0 0.4370 6.279 74.5 4.0522 5 398 18.7 373.66
## 78 0.08707 0.0 12.83 0 0.4370 6.140 45.8 4.0905 5 398 18.7 386.96
## 79 0.05646 0.0 12.83 0 0.4370 6.232 53.7 5.0141 5 398 18.7 386.40
## 80 0.08387 0.0 12.83 0 0.4370 5.874 36.6 4.5026 5 398 18.7 396.06
## 81 0.04113 25.0 4.86 0 0.4260 6.727 33.5 5.4007 4 281 19.0 396.90
## 82 0.04462 25.0 4.86 0 0.4260 6.619 70.4 5.4007 4 281 19.0 395.63
## 83 0.03659 25.0 4.86 0 0.4260 6.302 32.2 5.4007 4 281 19.0 396.90
## 84 0.03551 25.0 4.86 0 0.4260 6.167 46.7 5.4007 4 281 19.0 390.64
## 85 0.05059 0.0 4.49 0 0.4490 6.389 48.0 4.7794 3 247 18.5 396.90
## 86 0.05735 0.0 4.49 0 0.4490 6.630 56.1 4.4377 3 247 18.5 392.30
## 87 0.05188 0.0 4.49 0 0.4490 6.015 45.1 4.4272 3 247 18.5 395.99
## 88 0.07151 0.0 4.49 0 0.4490 6.121 56.8 3.7476 3 247 18.5 395.15
## 89 0.05660 0.0 3.41 0 0.4890 7.007 86.3 3.4217 2 270 17.8 396.90
## 90 0.05302 0.0 3.41 0 0.4890 7.079 63.1 3.4145 2 270 17.8 396.06
## 91 0.04684 0.0 3.41 0 0.4890 6.417 66.1 3.0923 2 270 17.8 392.18
## 92 0.03932 0.0 3.41 0 0.4890 6.405 73.9 3.0921 2 270 17.8 393.55
## 93 0.04203 28.0 15.04 0 0.4640 6.442 53.6 3.6659 4 270 18.2 395.01
## 94 0.02875 28.0 15.04 0 0.4640 6.211 28.9 3.6659 4 270 18.2 396.33
## 95 0.04294 28.0 15.04 0 0.4640 6.249 77.3 3.6150 4 270 18.2 396.90
## 96 0.12204 0.0 2.89 0 0.4450 6.625 57.8 3.4952 2 276 18.0 357.98
## 97 0.11504 0.0 2.89 0 0.4450 6.163 69.6 3.4952 2 276 18.0 391.83
## 98 0.12083 0.0 2.89 0 0.4450 8.069 76.0 3.4952 2 276 18.0 396.90
## 99 0.08187 0.0 2.89 0 0.4450 7.820 36.9 3.4952 2 276 18.0 393.53
## 100 0.06860 0.0 2.89 0 0.4450 7.416 62.5 3.4952 2 276 18.0 396.90
## 101 0.14866 0.0 8.56 0 0.5200 6.727 79.9 2.7778 5 384 20.9 394.76
## 102 0.11432 0.0 8.56 0 0.5200 6.781 71.3 2.8561 5 384 20.9 395.58
## 103 0.22876 0.0 8.56 0 0.5200 6.405 85.4 2.7147 5 384 20.9 70.80
## 104 0.21161 0.0 8.56 0 0.5200 6.137 87.4 2.7147 5 384 20.9 394.47
## 105 0.13960 0.0 8.56 0 0.5200 6.167 90.0 2.4210 5 384 20.9 392.69
## 106 0.13262 0.0 8.56 0 0.5200 5.851 96.7 2.1069 5 384 20.9 394.05
## 107 0.17120 0.0 8.56 0 0.5200 5.836 91.9 2.2110 5 384 20.9 395.67
## 108 0.13117 0.0 8.56 0 0.5200 6.127 85.2 2.1224 5 384 20.9 387.69
## 109 0.12802 0.0 8.56 0 0.5200 6.474 97.1 2.4329 5 384 20.9 395.24
## 110 0.26363 0.0 8.56 0 0.5200 6.229 91.2 2.5451 5 384 20.9 391.23
## 111 0.10793 0.0 8.56 0 0.5200 6.195 54.4 2.7778 5 384 20.9 393.49
## 112 0.10084 0.0 10.01 0 0.5470 6.715 81.6 2.6775 6 432 17.8 395.59
## 113 0.12329 0.0 10.01 0 0.5470 5.913 92.9 2.3534 6 432 17.8 394.95
## 114 0.22212 0.0 10.01 0 0.5470 6.092 95.4 2.5480 6 432 17.8 396.90
## 115 0.14231 0.0 10.01 0 0.5470 6.254 84.2 2.2565 6 432 17.8 388.74
## 116 0.17134 0.0 10.01 0 0.5470 5.928 88.2 2.4631 6 432 17.8 344.91
## 117 0.13158 0.0 10.01 0 0.5470 6.176 72.5 2.7301 6 432 17.8 393.30
## 118 0.15098 0.0 10.01 0 0.5470 6.021 82.6 2.7474 6 432 17.8 394.51
## 119 0.13058 0.0 10.01 0 0.5470 5.872 73.1 2.4775 6 432 17.8 338.63
## 120 0.14476 0.0 10.01 0 0.5470 5.731 65.2 2.7592 6 432 17.8 391.50
## 121 0.06899 0.0 25.65 0 0.5810 5.870 69.7 2.2577 2 188 19.1 389.15
## 122 0.07165 0.0 25.65 0 0.5810 6.004 84.1 2.1974 2 188 19.1 377.67
## 123 0.09299 0.0 25.65 0 0.5810 5.961 92.9 2.0869 2 188 19.1 378.09
## 124 0.15038 0.0 25.65 0 0.5810 5.856 97.0 1.9444 2 188 19.1 370.31
## 125 0.09849 0.0 25.65 0 0.5810 5.879 95.8 2.0063 2 188 19.1 379.38
## 126 0.16902 0.0 25.65 0 0.5810 5.986 88.4 1.9929 2 188 19.1 385.02
## 127 0.38735 0.0 25.65 0 0.5810 5.613 95.6 1.7572 2 188 19.1 359.29
## 128 0.25915 0.0 21.89 0 0.6240 5.693 96.0 1.7883 4 437 21.2 392.11
## 129 0.32543 0.0 21.89 0 0.6240 6.431 98.8 1.8125 4 437 21.2 396.90
## 130 0.88125 0.0 21.89 0 0.6240 5.637 94.7 1.9799 4 437 21.2 396.90
## 131 0.34006 0.0 21.89 0 0.6240 6.458 98.9 2.1185 4 437 21.2 395.04
## 132 1.19294 0.0 21.89 0 0.6240 6.326 97.7 2.2710 4 437 21.2 396.90
## 133 0.59005 0.0 21.89 0 0.6240 6.372 97.9 2.3274 4 437 21.2 385.76
## 134 0.32982 0.0 21.89 0 0.6240 5.822 95.4 2.4699 4 437 21.2 388.69
## 135 0.97617 0.0 21.89 0 0.6240 5.757 98.4 2.3460 4 437 21.2 262.76
## 136 0.55778 0.0 21.89 0 0.6240 6.335 98.2 2.1107 4 437 21.2 394.67
## 137 0.32264 0.0 21.89 0 0.6240 5.942 93.5 1.9669 4 437 21.2 378.25
## 138 0.35233 0.0 21.89 0 0.6240 6.454 98.4 1.8498 4 437 21.2 394.08
## 139 0.24980 0.0 21.89 0 0.6240 5.857 98.2 1.6686 4 437 21.2 392.04
## 140 0.54452 0.0 21.89 0 0.6240 6.151 97.9 1.6687 4 437 21.2 396.90
## 141 0.29090 0.0 21.89 0 0.6240 6.174 93.6 1.6119 4 437 21.2 388.08
## 142 1.62864 0.0 21.89 0 0.6240 5.019 100.0 1.4394 4 437 21.2 396.90
## 143 3.32105 0.0 19.58 1 0.8710 5.403 100.0 1.3216 5 403 14.7 396.90
## 144 4.09740 0.0 19.58 0 0.8710 5.468 100.0 1.4118 5 403 14.7 396.90
## 145 2.77974 0.0 19.58 0 0.8710 4.903 97.8 1.3459 5 403 14.7 396.90
## 146 2.37934 0.0 19.58 0 0.8710 6.130 100.0 1.4191 5 403 14.7 172.91
## 147 2.15505 0.0 19.58 0 0.8710 5.628 100.0 1.5166 5 403 14.7 169.27
## 148 2.36862 0.0 19.58 0 0.8710 4.926 95.7 1.4608 5 403 14.7 391.71
## 149 2.33099 0.0 19.58 0 0.8710 5.186 93.8 1.5296 5 403 14.7 356.99
## 150 2.73397 0.0 19.58 0 0.8710 5.597 94.9 1.5257 5 403 14.7 351.85
## 151 1.65660 0.0 19.58 0 0.8710 6.122 97.3 1.6180 5 403 14.7 372.80
## 152 1.49632 0.0 19.58 0 0.8710 5.404 100.0 1.5916 5 403 14.7 341.60
## 153 1.12658 0.0 19.58 1 0.8710 5.012 88.0 1.6102 5 403 14.7 343.28
## 154 2.14918 0.0 19.58 0 0.8710 5.709 98.5 1.6232 5 403 14.7 261.95
## 155 1.41385 0.0 19.58 1 0.8710 6.129 96.0 1.7494 5 403 14.7 321.02
## 156 3.53501 0.0 19.58 1 0.8710 6.152 82.6 1.7455 5 403 14.7 88.01
## 157 2.44668 0.0 19.58 0 0.8710 5.272 94.0 1.7364 5 403 14.7 88.63
## 158 1.22358 0.0 19.58 0 0.6050 6.943 97.4 1.8773 5 403 14.7 363.43
## 159 1.34284 0.0 19.58 0 0.6050 6.066 100.0 1.7573 5 403 14.7 353.89
## 160 1.42502 0.0 19.58 0 0.8710 6.510 100.0 1.7659 5 403 14.7 364.31
## 161 1.27346 0.0 19.58 1 0.6050 6.250 92.6 1.7984 5 403 14.7 338.92
## 162 1.46336 0.0 19.58 0 0.6050 7.489 90.8 1.9709 5 403 14.7 374.43
## 163 1.83377 0.0 19.58 1 0.6050 7.802 98.2 2.0407 5 403 14.7 389.61
## 164 1.51902 0.0 19.58 1 0.6050 8.375 93.9 2.1620 5 403 14.7 388.45
## 165 2.24236 0.0 19.58 0 0.6050 5.854 91.8 2.4220 5 403 14.7 395.11
## 166 2.92400 0.0 19.58 0 0.6050 6.101 93.0 2.2834 5 403 14.7 240.16
## 167 2.01019 0.0 19.58 0 0.6050 7.929 96.2 2.0459 5 403 14.7 369.30
## 168 1.80028 0.0 19.58 0 0.6050 5.877 79.2 2.4259 5 403 14.7 227.61
## 169 2.30040 0.0 19.58 0 0.6050 6.319 96.1 2.1000 5 403 14.7 297.09
## 170 2.44953 0.0 19.58 0 0.6050 6.402 95.2 2.2625 5 403 14.7 330.04
## 171 1.20742 0.0 19.58 0 0.6050 5.875 94.6 2.4259 5 403 14.7 292.29
## 172 2.31390 0.0 19.58 0 0.6050 5.880 97.3 2.3887 5 403 14.7 348.13
## 173 0.13914 0.0 4.05 0 0.5100 5.572 88.5 2.5961 5 296 16.6 396.90
## 174 0.09178 0.0 4.05 0 0.5100 6.416 84.1 2.6463 5 296 16.6 395.50
## 175 0.08447 0.0 4.05 0 0.5100 5.859 68.7 2.7019 5 296 16.6 393.23
## 176 0.06664 0.0 4.05 0 0.5100 6.546 33.1 3.1323 5 296 16.6 390.96
## 177 0.07022 0.0 4.05 0 0.5100 6.020 47.2 3.5549 5 296 16.6 393.23
## 178 0.05425 0.0 4.05 0 0.5100 6.315 73.4 3.3175 5 296 16.6 395.60
## 179 0.06642 0.0 4.05 0 0.5100 6.860 74.4 2.9153 5 296 16.6 391.27
## 180 0.05780 0.0 2.46 0 0.4880 6.980 58.4 2.8290 3 193 17.8 396.90
## 181 0.06588 0.0 2.46 0 0.4880 7.765 83.3 2.7410 3 193 17.8 395.56
## 182 0.06888 0.0 2.46 0 0.4880 6.144 62.2 2.5979 3 193 17.8 396.90
## 183 0.09103 0.0 2.46 0 0.4880 7.155 92.2 2.7006 3 193 17.8 394.12
## 184 0.10008 0.0 2.46 0 0.4880 6.563 95.6 2.8470 3 193 17.8 396.90
## 185 0.08308 0.0 2.46 0 0.4880 5.604 89.8 2.9879 3 193 17.8 391.00
## 186 0.06047 0.0 2.46 0 0.4880 6.153 68.8 3.2797 3 193 17.8 387.11
## 187 0.05602 0.0 2.46 0 0.4880 7.831 53.6 3.1992 3 193 17.8 392.63
## 188 0.07875 45.0 3.44 0 0.4370 6.782 41.1 3.7886 5 398 15.2 393.87
## 189 0.12579 45.0 3.44 0 0.4370 6.556 29.1 4.5667 5 398 15.2 382.84
## 190 0.08370 45.0 3.44 0 0.4370 7.185 38.9 4.5667 5 398 15.2 396.90
## 191 0.09068 45.0 3.44 0 0.4370 6.951 21.5 6.4798 5 398 15.2 377.68
## 192 0.06911 45.0 3.44 0 0.4370 6.739 30.8 6.4798 5 398 15.2 389.71
## 193 0.08664 45.0 3.44 0 0.4370 7.178 26.3 6.4798 5 398 15.2 390.49
## 194 0.02187 60.0 2.93 0 0.4010 6.800 9.9 6.2196 1 265 15.6 393.37
## 195 0.01439 60.0 2.93 0 0.4010 6.604 18.8 6.2196 1 265 15.6 376.70
## 196 0.01381 80.0 0.46 0 0.4220 7.875 32.0 5.6484 4 255 14.4 394.23
## 197 0.04011 80.0 1.52 0 0.4040 7.287 34.1 7.3090 2 329 12.6 396.90
## 198 0.04666 80.0 1.52 0 0.4040 7.107 36.6 7.3090 2 329 12.6 354.31
## 199 0.03768 80.0 1.52 0 0.4040 7.274 38.3 7.3090 2 329 12.6 392.20
## 200 0.03150 95.0 1.47 0 0.4030 6.975 15.3 7.6534 3 402 17.0 396.90
## 201 0.01778 95.0 1.47 0 0.4030 7.135 13.9 7.6534 3 402 17.0 384.30
## 202 0.03445 82.5 2.03 0 0.4150 6.162 38.4 6.2700 2 348 14.7 393.77
## 203 0.02177 82.5 2.03 0 0.4150 7.610 15.7 6.2700 2 348 14.7 395.38
## 204 0.03510 95.0 2.68 0 0.4161 7.853 33.2 5.1180 4 224 14.7 392.78
## 205 0.02009 95.0 2.68 0 0.4161 8.034 31.9 5.1180 4 224 14.7 390.55
## 206 0.13642 0.0 10.59 0 0.4890 5.891 22.3 3.9454 4 277 18.6 396.90
## 207 0.22969 0.0 10.59 0 0.4890 6.326 52.5 4.3549 4 277 18.6 394.87
## 208 0.25199 0.0 10.59 0 0.4890 5.783 72.7 4.3549 4 277 18.6 389.43
## 209 0.13587 0.0 10.59 1 0.4890 6.064 59.1 4.2392 4 277 18.6 381.32
## 210 0.43571 0.0 10.59 1 0.4890 5.344 100.0 3.8750 4 277 18.6 396.90
## 211 0.17446 0.0 10.59 1 0.4890 5.960 92.1 3.8771 4 277 18.6 393.25
## 212 0.37578 0.0 10.59 1 0.4890 5.404 88.6 3.6650 4 277 18.6 395.24
## 213 0.21719 0.0 10.59 1 0.4890 5.807 53.8 3.6526 4 277 18.6 390.94
## 214 0.14052 0.0 10.59 0 0.4890 6.375 32.3 3.9454 4 277 18.6 385.81
## 215 0.28955 0.0 10.59 0 0.4890 5.412 9.8 3.5875 4 277 18.6 348.93
## 216 0.19802 0.0 10.59 0 0.4890 6.182 42.4 3.9454 4 277 18.6 393.63
## 217 0.04560 0.0 13.89 1 0.5500 5.888 56.0 3.1121 5 276 16.4 392.80
## 218 0.07013 0.0 13.89 0 0.5500 6.642 85.1 3.4211 5 276 16.4 392.78
## 219 0.11069 0.0 13.89 1 0.5500 5.951 93.8 2.8893 5 276 16.4 396.90
## 220 0.11425 0.0 13.89 1 0.5500 6.373 92.4 3.3633 5 276 16.4 393.74
## 221 0.35809 0.0 6.20 1 0.5070 6.951 88.5 2.8617 8 307 17.4 391.70
## 222 0.40771 0.0 6.20 1 0.5070 6.164 91.3 3.0480 8 307 17.4 395.24
## 223 0.62356 0.0 6.20 1 0.5070 6.879 77.7 3.2721 8 307 17.4 390.39
## 224 0.61470 0.0 6.20 0 0.5070 6.618 80.8 3.2721 8 307 17.4 396.90
## 225 0.31533 0.0 6.20 0 0.5040 8.266 78.3 2.8944 8 307 17.4 385.05
## 226 0.52693 0.0 6.20 0 0.5040 8.725 83.0 2.8944 8 307 17.4 382.00
## 227 0.38214 0.0 6.20 0 0.5040 8.040 86.5 3.2157 8 307 17.4 387.38
## 228 0.41238 0.0 6.20 0 0.5040 7.163 79.9 3.2157 8 307 17.4 372.08
## 229 0.29819 0.0 6.20 0 0.5040 7.686 17.0 3.3751 8 307 17.4 377.51
## 230 0.44178 0.0 6.20 0 0.5040 6.552 21.4 3.3751 8 307 17.4 380.34
## 231 0.53700 0.0 6.20 0 0.5040 5.981 68.1 3.6715 8 307 17.4 378.35
## 232 0.46296 0.0 6.20 0 0.5040 7.412 76.9 3.6715 8 307 17.4 376.14
## 233 0.57529 0.0 6.20 0 0.5070 8.337 73.3 3.8384 8 307 17.4 385.91
## 234 0.33147 0.0 6.20 0 0.5070 8.247 70.4 3.6519 8 307 17.4 378.95
## 235 0.44791 0.0 6.20 1 0.5070 6.726 66.5 3.6519 8 307 17.4 360.20
## 236 0.33045 0.0 6.20 0 0.5070 6.086 61.5 3.6519 8 307 17.4 376.75
## 237 0.52058 0.0 6.20 1 0.5070 6.631 76.5 4.1480 8 307 17.4 388.45
## 238 0.51183 0.0 6.20 0 0.5070 7.358 71.6 4.1480 8 307 17.4 390.07
## 239 0.08244 30.0 4.93 0 0.4280 6.481 18.5 6.1899 6 300 16.6 379.41
## 240 0.09252 30.0 4.93 0 0.4280 6.606 42.2 6.1899 6 300 16.6 383.78
## 241 0.11329 30.0 4.93 0 0.4280 6.897 54.3 6.3361 6 300 16.6 391.25
## 242 0.10612 30.0 4.93 0 0.4280 6.095 65.1 6.3361 6 300 16.6 394.62
## 243 0.10290 30.0 4.93 0 0.4280 6.358 52.9 7.0355 6 300 16.6 372.75
## 244 0.12757 30.0 4.93 0 0.4280 6.393 7.8 7.0355 6 300 16.6 374.71
## 245 0.20608 22.0 5.86 0 0.4310 5.593 76.5 7.9549 7 330 19.1 372.49
## 246 0.19133 22.0 5.86 0 0.4310 5.605 70.2 7.9549 7 330 19.1 389.13
## 247 0.33983 22.0 5.86 0 0.4310 6.108 34.9 8.0555 7 330 19.1 390.18
## 248 0.19657 22.0 5.86 0 0.4310 6.226 79.2 8.0555 7 330 19.1 376.14
## 249 0.16439 22.0 5.86 0 0.4310 6.433 49.1 7.8265 7 330 19.1 374.71
## 250 0.19073 22.0 5.86 0 0.4310 6.718 17.5 7.8265 7 330 19.1 393.74
## 251 0.14030 22.0 5.86 0 0.4310 6.487 13.0 7.3967 7 330 19.1 396.28
## 252 0.21409 22.0 5.86 0 0.4310 6.438 8.9 7.3967 7 330 19.1 377.07
## 253 0.08221 22.0 5.86 0 0.4310 6.957 6.8 8.9067 7 330 19.1 386.09
## 254 0.36894 22.0 5.86 0 0.4310 8.259 8.4 8.9067 7 330 19.1 396.90
## 255 0.04819 80.0 3.64 0 0.3920 6.108 32.0 9.2203 1 315 16.4 392.89
## 256 0.03548 80.0 3.64 0 0.3920 5.876 19.1 9.2203 1 315 16.4 395.18
## 257 0.01538 90.0 3.75 0 0.3940 7.454 34.2 6.3361 3 244 15.9 386.34
## 258 0.61154 20.0 3.97 0 0.6470 8.704 86.9 1.8010 5 264 13.0 389.70
## 259 0.66351 20.0 3.97 0 0.6470 7.333 100.0 1.8946 5 264 13.0 383.29
## 260 0.65665 20.0 3.97 0 0.6470 6.842 100.0 2.0107 5 264 13.0 391.93
## 261 0.54011 20.0 3.97 0 0.6470 7.203 81.8 2.1121 5 264 13.0 392.80
## 262 0.53412 20.0 3.97 0 0.6470 7.520 89.4 2.1398 5 264 13.0 388.37
## 263 0.52014 20.0 3.97 0 0.6470 8.398 91.5 2.2885 5 264 13.0 386.86
## 264 0.82526 20.0 3.97 0 0.6470 7.327 94.5 2.0788 5 264 13.0 393.42
## 265 0.55007 20.0 3.97 0 0.6470 7.206 91.6 1.9301 5 264 13.0 387.89
## 266 0.76162 20.0 3.97 0 0.6470 5.560 62.8 1.9865 5 264 13.0 392.40
## 267 0.78570 20.0 3.97 0 0.6470 7.014 84.6 2.1329 5 264 13.0 384.07
## 268 0.57834 20.0 3.97 0 0.5750 8.297 67.0 2.4216 5 264 13.0 384.54
## 269 0.54050 20.0 3.97 0 0.5750 7.470 52.6 2.8720 5 264 13.0 390.30
## 270 0.09065 20.0 6.96 1 0.4640 5.920 61.5 3.9175 3 223 18.6 391.34
## 271 0.29916 20.0 6.96 0 0.4640 5.856 42.1 4.4290 3 223 18.6 388.65
## 272 0.16211 20.0 6.96 0 0.4640 6.240 16.3 4.4290 3 223 18.6 396.90
## 273 0.11460 20.0 6.96 0 0.4640 6.538 58.7 3.9175 3 223 18.6 394.96
## 274 0.22188 20.0 6.96 1 0.4640 7.691 51.8 4.3665 3 223 18.6 390.77
## 275 0.05644 40.0 6.41 1 0.4470 6.758 32.9 4.0776 4 254 17.6 396.90
## 276 0.09604 40.0 6.41 0 0.4470 6.854 42.8 4.2673 4 254 17.6 396.90
## 277 0.10469 40.0 6.41 1 0.4470 7.267 49.0 4.7872 4 254 17.6 389.25
## 278 0.06127 40.0 6.41 1 0.4470 6.826 27.6 4.8628 4 254 17.6 393.45
## 279 0.07978 40.0 6.41 0 0.4470 6.482 32.1 4.1403 4 254 17.6 396.90
## 280 0.21038 20.0 3.33 0 0.4429 6.812 32.2 4.1007 5 216 14.9 396.90
## 281 0.03578 20.0 3.33 0 0.4429 7.820 64.5 4.6947 5 216 14.9 387.31
## 282 0.03705 20.0 3.33 0 0.4429 6.968 37.2 5.2447 5 216 14.9 392.23
## 283 0.06129 20.0 3.33 1 0.4429 7.645 49.7 5.2119 5 216 14.9 377.07
## 284 0.01501 90.0 1.21 1 0.4010 7.923 24.8 5.8850 1 198 13.6 395.52
## 285 0.00906 90.0 2.97 0 0.4000 7.088 20.8 7.3073 1 285 15.3 394.72
## 286 0.01096 55.0 2.25 0 0.3890 6.453 31.9 7.3073 1 300 15.3 394.72
## 287 0.01965 80.0 1.76 0 0.3850 6.230 31.5 9.0892 1 241 18.2 341.60
## 288 0.03871 52.5 5.32 0 0.4050 6.209 31.3 7.3172 6 293 16.6 396.90
## 289 0.04590 52.5 5.32 0 0.4050 6.315 45.6 7.3172 6 293 16.6 396.90
## 290 0.04297 52.5 5.32 0 0.4050 6.565 22.9 7.3172 6 293 16.6 371.72
## 291 0.03502 80.0 4.95 0 0.4110 6.861 27.9 5.1167 4 245 19.2 396.90
## 292 0.07886 80.0 4.95 0 0.4110 7.148 27.7 5.1167 4 245 19.2 396.90
## 293 0.03615 80.0 4.95 0 0.4110 6.630 23.4 5.1167 4 245 19.2 396.90
## 294 0.08265 0.0 13.92 0 0.4370 6.127 18.4 5.5027 4 289 16.0 396.90
## 295 0.08199 0.0 13.92 0 0.4370 6.009 42.3 5.5027 4 289 16.0 396.90
## 296 0.12932 0.0 13.92 0 0.4370 6.678 31.1 5.9604 4 289 16.0 396.90
## 297 0.05372 0.0 13.92 0 0.4370 6.549 51.0 5.9604 4 289 16.0 392.85
## 298 0.14103 0.0 13.92 0 0.4370 5.790 58.0 6.3200 4 289 16.0 396.90
## 299 0.06466 70.0 2.24 0 0.4000 6.345 20.1 7.8278 5 358 14.8 368.24
## 300 0.05561 70.0 2.24 0 0.4000 7.041 10.0 7.8278 5 358 14.8 371.58
## 301 0.04417 70.0 2.24 0 0.4000 6.871 47.4 7.8278 5 358 14.8 390.86
## 302 0.03537 34.0 6.09 0 0.4330 6.590 40.4 5.4917 7 329 16.1 395.75
## 303 0.09266 34.0 6.09 0 0.4330 6.495 18.4 5.4917 7 329 16.1 383.61
## 304 0.10000 34.0 6.09 0 0.4330 6.982 17.7 5.4917 7 329 16.1 390.43
## 305 0.05515 33.0 2.18 0 0.4720 7.236 41.1 4.0220 7 222 18.4 393.68
## 306 0.05479 33.0 2.18 0 0.4720 6.616 58.1 3.3700 7 222 18.4 393.36
## 307 0.07503 33.0 2.18 0 0.4720 7.420 71.9 3.0992 7 222 18.4 396.90
## 308 0.04932 33.0 2.18 0 0.4720 6.849 70.3 3.1827 7 222 18.4 396.90
## 309 0.49298 0.0 9.90 0 0.5440 6.635 82.5 3.3175 4 304 18.4 396.90
## 310 0.34940 0.0 9.90 0 0.5440 5.972 76.7 3.1025 4 304 18.4 396.24
## 311 2.63548 0.0 9.90 0 0.5440 4.973 37.8 2.5194 4 304 18.4 350.45
## 312 0.79041 0.0 9.90 0 0.5440 6.122 52.8 2.6403 4 304 18.4 396.90
## 313 0.26169 0.0 9.90 0 0.5440 6.023 90.4 2.8340 4 304 18.4 396.30
## 314 0.26938 0.0 9.90 0 0.5440 6.266 82.8 3.2628 4 304 18.4 393.39
## 315 0.36920 0.0 9.90 0 0.5440 6.567 87.3 3.6023 4 304 18.4 395.69
## 316 0.25356 0.0 9.90 0 0.5440 5.705 77.7 3.9450 4 304 18.4 396.42
## 317 0.31827 0.0 9.90 0 0.5440 5.914 83.2 3.9986 4 304 18.4 390.70
## 318 0.24522 0.0 9.90 0 0.5440 5.782 71.7 4.0317 4 304 18.4 396.90
## 319 0.40202 0.0 9.90 0 0.5440 6.382 67.2 3.5325 4 304 18.4 395.21
## 320 0.47547 0.0 9.90 0 0.5440 6.113 58.8 4.0019 4 304 18.4 396.23
## 321 0.16760 0.0 7.38 0 0.4930 6.426 52.3 4.5404 5 287 19.6 396.90
## 322 0.18159 0.0 7.38 0 0.4930 6.376 54.3 4.5404 5 287 19.6 396.90
## 323 0.35114 0.0 7.38 0 0.4930 6.041 49.9 4.7211 5 287 19.6 396.90
## 324 0.28392 0.0 7.38 0 0.4930 5.708 74.3 4.7211 5 287 19.6 391.13
## 325 0.34109 0.0 7.38 0 0.4930 6.415 40.1 4.7211 5 287 19.6 396.90
## 326 0.19186 0.0 7.38 0 0.4930 6.431 14.7 5.4159 5 287 19.6 393.68
## 327 0.30347 0.0 7.38 0 0.4930 6.312 28.9 5.4159 5 287 19.6 396.90
## 328 0.24103 0.0 7.38 0 0.4930 6.083 43.7 5.4159 5 287 19.6 396.90
## 329 0.06617 0.0 3.24 0 0.4600 5.868 25.8 5.2146 4 430 16.9 382.44
## 330 0.06724 0.0 3.24 0 0.4600 6.333 17.2 5.2146 4 430 16.9 375.21
## 331 0.04544 0.0 3.24 0 0.4600 6.144 32.2 5.8736 4 430 16.9 368.57
## 332 0.05023 35.0 6.06 0 0.4379 5.706 28.4 6.6407 1 304 16.9 394.02
## 333 0.03466 35.0 6.06 0 0.4379 6.031 23.3 6.6407 1 304 16.9 362.25
## 334 0.05083 0.0 5.19 0 0.5150 6.316 38.1 6.4584 5 224 20.2 389.71
## 335 0.03738 0.0 5.19 0 0.5150 6.310 38.5 6.4584 5 224 20.2 389.40
## 336 0.03961 0.0 5.19 0 0.5150 6.037 34.5 5.9853 5 224 20.2 396.90
## 337 0.03427 0.0 5.19 0 0.5150 5.869 46.3 5.2311 5 224 20.2 396.90
## 338 0.03041 0.0 5.19 0 0.5150 5.895 59.6 5.6150 5 224 20.2 394.81
## 339 0.03306 0.0 5.19 0 0.5150 6.059 37.3 4.8122 5 224 20.2 396.14
## 340 0.05497 0.0 5.19 0 0.5150 5.985 45.4 4.8122 5 224 20.2 396.90
## 341 0.06151 0.0 5.19 0 0.5150 5.968 58.5 4.8122 5 224 20.2 396.90
## 342 0.01301 35.0 1.52 0 0.4420 7.241 49.3 7.0379 1 284 15.5 394.74
## 343 0.02498 0.0 1.89 0 0.5180 6.540 59.7 6.2669 1 422 15.9 389.96
## 344 0.02543 55.0 3.78 0 0.4840 6.696 56.4 5.7321 5 370 17.6 396.90
## 345 0.03049 55.0 3.78 0 0.4840 6.874 28.1 6.4654 5 370 17.6 387.97
## 346 0.03113 0.0 4.39 0 0.4420 6.014 48.5 8.0136 3 352 18.8 385.64
## 347 0.06162 0.0 4.39 0 0.4420 5.898 52.3 8.0136 3 352 18.8 364.61
## 348 0.01870 85.0 4.15 0 0.4290 6.516 27.7 8.5353 4 351 17.9 392.43
## 349 0.01501 80.0 2.01 0 0.4350 6.635 29.7 8.3440 4 280 17.0 390.94
## 350 0.02899 40.0 1.25 0 0.4290 6.939 34.5 8.7921 1 335 19.7 389.85
## 351 0.06211 40.0 1.25 0 0.4290 6.490 44.4 8.7921 1 335 19.7 396.90
## 352 0.07950 60.0 1.69 0 0.4110 6.579 35.9 10.7103 4 411 18.3 370.78
## 353 0.07244 60.0 1.69 0 0.4110 5.884 18.5 10.7103 4 411 18.3 392.33
## 354 0.01709 90.0 2.02 0 0.4100 6.728 36.1 12.1265 5 187 17.0 384.46
## 355 0.04301 80.0 1.91 0 0.4130 5.663 21.9 10.5857 4 334 22.0 382.80
## 356 0.10659 80.0 1.91 0 0.4130 5.936 19.5 10.5857 4 334 22.0 376.04
## 357 8.98296 0.0 18.10 1 0.7700 6.212 97.4 2.1222 24 666 20.2 377.73
## 358 3.84970 0.0 18.10 1 0.7700 6.395 91.0 2.5052 24 666 20.2 391.34
## 359 5.20177 0.0 18.10 1 0.7700 6.127 83.4 2.7227 24 666 20.2 395.43
## 360 4.26131 0.0 18.10 0 0.7700 6.112 81.3 2.5091 24 666 20.2 390.74
## 361 4.54192 0.0 18.10 0 0.7700 6.398 88.0 2.5182 24 666 20.2 374.56
## 362 3.83684 0.0 18.10 0 0.7700 6.251 91.1 2.2955 24 666 20.2 350.65
## 363 3.67822 0.0 18.10 0 0.7700 5.362 96.2 2.1036 24 666 20.2 380.79
## 364 4.22239 0.0 18.10 1 0.7700 5.803 89.0 1.9047 24 666 20.2 353.04
## 365 3.47428 0.0 18.10 1 0.7180 8.780 82.9 1.9047 24 666 20.2 354.55
## 366 4.55587 0.0 18.10 0 0.7180 3.561 87.9 1.6132 24 666 20.2 354.70
## 367 3.69695 0.0 18.10 0 0.7180 4.963 91.4 1.7523 24 666 20.2 316.03
## 368 13.52220 0.0 18.10 0 0.6310 3.863 100.0 1.5106 24 666 20.2 131.42
## 369 4.89822 0.0 18.10 0 0.6310 4.970 100.0 1.3325 24 666 20.2 375.52
## 370 5.66998 0.0 18.10 1 0.6310 6.683 96.8 1.3567 24 666 20.2 375.33
## 371 6.53876 0.0 18.10 1 0.6310 7.016 97.5 1.2024 24 666 20.2 392.05
## 372 9.23230 0.0 18.10 0 0.6310 6.216 100.0 1.1691 24 666 20.2 366.15
## 373 8.26725 0.0 18.10 1 0.6680 5.875 89.6 1.1296 24 666 20.2 347.88
## 374 11.10810 0.0 18.10 0 0.6680 4.906 100.0 1.1742 24 666 20.2 396.90
## 375 18.49820 0.0 18.10 0 0.6680 4.138 100.0 1.1370 24 666 20.2 396.90
## 376 19.60910 0.0 18.10 0 0.6710 7.313 97.9 1.3163 24 666 20.2 396.90
## 377 15.28800 0.0 18.10 0 0.6710 6.649 93.3 1.3449 24 666 20.2 363.02
## 378 9.82349 0.0 18.10 0 0.6710 6.794 98.8 1.3580 24 666 20.2 396.90
## 379 23.64820 0.0 18.10 0 0.6710 6.380 96.2 1.3861 24 666 20.2 396.90
## 380 17.86670 0.0 18.10 0 0.6710 6.223 100.0 1.3861 24 666 20.2 393.74
## 381 88.97620 0.0 18.10 0 0.6710 6.968 91.9 1.4165 24 666 20.2 396.90
## 382 15.87440 0.0 18.10 0 0.6710 6.545 99.1 1.5192 24 666 20.2 396.90
## 383 9.18702 0.0 18.10 0 0.7000 5.536 100.0 1.5804 24 666 20.2 396.90
## 384 7.99248 0.0 18.10 0 0.7000 5.520 100.0 1.5331 24 666 20.2 396.90
## 385 20.08490 0.0 18.10 0 0.7000 4.368 91.2 1.4395 24 666 20.2 285.83
## 386 16.81180 0.0 18.10 0 0.7000 5.277 98.1 1.4261 24 666 20.2 396.90
## 387 24.39380 0.0 18.10 0 0.7000 4.652 100.0 1.4672 24 666 20.2 396.90
## 388 22.59710 0.0 18.10 0 0.7000 5.000 89.5 1.5184 24 666 20.2 396.90
## 389 14.33370 0.0 18.10 0 0.7000 4.880 100.0 1.5895 24 666 20.2 372.92
## 390 8.15174 0.0 18.10 0 0.7000 5.390 98.9 1.7281 24 666 20.2 396.90
## 391 6.96215 0.0 18.10 0 0.7000 5.713 97.0 1.9265 24 666 20.2 394.43
## 392 5.29305 0.0 18.10 0 0.7000 6.051 82.5 2.1678 24 666 20.2 378.38
## 393 11.57790 0.0 18.10 0 0.7000 5.036 97.0 1.7700 24 666 20.2 396.90
## 394 8.64476 0.0 18.10 0 0.6930 6.193 92.6 1.7912 24 666 20.2 396.90
## 395 13.35980 0.0 18.10 0 0.6930 5.887 94.7 1.7821 24 666 20.2 396.90
## 396 8.71675 0.0 18.10 0 0.6930 6.471 98.8 1.7257 24 666 20.2 391.98
## 397 5.87205 0.0 18.10 0 0.6930 6.405 96.0 1.6768 24 666 20.2 396.90
## 398 7.67202 0.0 18.10 0 0.6930 5.747 98.9 1.6334 24 666 20.2 393.10
## 399 38.35180 0.0 18.10 0 0.6930 5.453 100.0 1.4896 24 666 20.2 396.90
## 400 9.91655 0.0 18.10 0 0.6930 5.852 77.8 1.5004 24 666 20.2 338.16
## 401 25.04610 0.0 18.10 0 0.6930 5.987 100.0 1.5888 24 666 20.2 396.90
## 402 14.23620 0.0 18.10 0 0.6930 6.343 100.0 1.5741 24 666 20.2 396.90
## 403 9.59571 0.0 18.10 0 0.6930 6.404 100.0 1.6390 24 666 20.2 376.11
## 404 24.80170 0.0 18.10 0 0.6930 5.349 96.0 1.7028 24 666 20.2 396.90
## 405 41.52920 0.0 18.10 0 0.6930 5.531 85.4 1.6074 24 666 20.2 329.46
## 406 67.92080 0.0 18.10 0 0.6930 5.683 100.0 1.4254 24 666 20.2 384.97
## 407 20.71620 0.0 18.10 0 0.6590 4.138 100.0 1.1781 24 666 20.2 370.22
## 408 11.95110 0.0 18.10 0 0.6590 5.608 100.0 1.2852 24 666 20.2 332.09
## 409 7.40389 0.0 18.10 0 0.5970 5.617 97.9 1.4547 24 666 20.2 314.64
## 410 14.43830 0.0 18.10 0 0.5970 6.852 100.0 1.4655 24 666 20.2 179.36
## 411 51.13580 0.0 18.10 0 0.5970 5.757 100.0 1.4130 24 666 20.2 2.60
## 412 14.05070 0.0 18.10 0 0.5970 6.657 100.0 1.5275 24 666 20.2 35.05
## 413 18.81100 0.0 18.10 0 0.5970 4.628 100.0 1.5539 24 666 20.2 28.79
## 414 28.65580 0.0 18.10 0 0.5970 5.155 100.0 1.5894 24 666 20.2 210.97
## 415 45.74610 0.0 18.10 0 0.6930 4.519 100.0 1.6582 24 666 20.2 88.27
## 416 18.08460 0.0 18.10 0 0.6790 6.434 100.0 1.8347 24 666 20.2 27.25
## 417 10.83420 0.0 18.10 0 0.6790 6.782 90.8 1.8195 24 666 20.2 21.57
## 418 25.94060 0.0 18.10 0 0.6790 5.304 89.1 1.6475 24 666 20.2 127.36
## 419 73.53410 0.0 18.10 0 0.6790 5.957 100.0 1.8026 24 666 20.2 16.45
## 420 11.81230 0.0 18.10 0 0.7180 6.824 76.5 1.7940 24 666 20.2 48.45
## 421 11.08740 0.0 18.10 0 0.7180 6.411 100.0 1.8589 24 666 20.2 318.75
## 422 7.02259 0.0 18.10 0 0.7180 6.006 95.3 1.8746 24 666 20.2 319.98
## 423 12.04820 0.0 18.10 0 0.6140 5.648 87.6 1.9512 24 666 20.2 291.55
## 424 7.05042 0.0 18.10 0 0.6140 6.103 85.1 2.0218 24 666 20.2 2.52
## 425 8.79212 0.0 18.10 0 0.5840 5.565 70.6 2.0635 24 666 20.2 3.65
## 426 15.86030 0.0 18.10 0 0.6790 5.896 95.4 1.9096 24 666 20.2 7.68
## 427 12.24720 0.0 18.10 0 0.5840 5.837 59.7 1.9976 24 666 20.2 24.65
## 428 37.66190 0.0 18.10 0 0.6790 6.202 78.7 1.8629 24 666 20.2 18.82
## 429 7.36711 0.0 18.10 0 0.6790 6.193 78.1 1.9356 24 666 20.2 96.73
## 430 9.33889 0.0 18.10 0 0.6790 6.380 95.6 1.9682 24 666 20.2 60.72
## 431 8.49213 0.0 18.10 0 0.5840 6.348 86.1 2.0527 24 666 20.2 83.45
## 432 10.06230 0.0 18.10 0 0.5840 6.833 94.3 2.0882 24 666 20.2 81.33
## 433 6.44405 0.0 18.10 0 0.5840 6.425 74.8 2.2004 24 666 20.2 97.95
## 434 5.58107 0.0 18.10 0 0.7130 6.436 87.9 2.3158 24 666 20.2 100.19
## 435 13.91340 0.0 18.10 0 0.7130 6.208 95.0 2.2222 24 666 20.2 100.63
## 436 11.16040 0.0 18.10 0 0.7400 6.629 94.6 2.1247 24 666 20.2 109.85
## 437 14.42080 0.0 18.10 0 0.7400 6.461 93.3 2.0026 24 666 20.2 27.49
## 438 15.17720 0.0 18.10 0 0.7400 6.152 100.0 1.9142 24 666 20.2 9.32
## 439 13.67810 0.0 18.10 0 0.7400 5.935 87.9 1.8206 24 666 20.2 68.95
## 440 9.39063 0.0 18.10 0 0.7400 5.627 93.9 1.8172 24 666 20.2 396.90
## 441 22.05110 0.0 18.10 0 0.7400 5.818 92.4 1.8662 24 666 20.2 391.45
## 442 9.72418 0.0 18.10 0 0.7400 6.406 97.2 2.0651 24 666 20.2 385.96
## 443 5.66637 0.0 18.10 0 0.7400 6.219 100.0 2.0048 24 666 20.2 395.69
## 444 9.96654 0.0 18.10 0 0.7400 6.485 100.0 1.9784 24 666 20.2 386.73
## 445 12.80230 0.0 18.10 0 0.7400 5.854 96.6 1.8956 24 666 20.2 240.52
## 446 10.67180 0.0 18.10 0 0.7400 6.459 94.8 1.9879 24 666 20.2 43.06
## 447 6.28807 0.0 18.10 0 0.7400 6.341 96.4 2.0720 24 666 20.2 318.01
## 448 9.92485 0.0 18.10 0 0.7400 6.251 96.6 2.1980 24 666 20.2 388.52
## 449 9.32909 0.0 18.10 0 0.7130 6.185 98.7 2.2616 24 666 20.2 396.90
## 450 7.52601 0.0 18.10 0 0.7130 6.417 98.3 2.1850 24 666 20.2 304.21
## 451 6.71772 0.0 18.10 0 0.7130 6.749 92.6 2.3236 24 666 20.2 0.32
## 452 5.44114 0.0 18.10 0 0.7130 6.655 98.2 2.3552 24 666 20.2 355.29
## 453 5.09017 0.0 18.10 0 0.7130 6.297 91.8 2.3682 24 666 20.2 385.09
## 454 8.24809 0.0 18.10 0 0.7130 7.393 99.3 2.4527 24 666 20.2 375.87
## 455 9.51363 0.0 18.10 0 0.7130 6.728 94.1 2.4961 24 666 20.2 6.68
## 456 4.75237 0.0 18.10 0 0.7130 6.525 86.5 2.4358 24 666 20.2 50.92
## 457 4.66883 0.0 18.10 0 0.7130 5.976 87.9 2.5806 24 666 20.2 10.48
## 458 8.20058 0.0 18.10 0 0.7130 5.936 80.3 2.7792 24 666 20.2 3.50
## 459 7.75223 0.0 18.10 0 0.7130 6.301 83.7 2.7831 24 666 20.2 272.21
## 460 6.80117 0.0 18.10 0 0.7130 6.081 84.4 2.7175 24 666 20.2 396.90
## 461 4.81213 0.0 18.10 0 0.7130 6.701 90.0 2.5975 24 666 20.2 255.23
## 462 3.69311 0.0 18.10 0 0.7130 6.376 88.4 2.5671 24 666 20.2 391.43
## 463 6.65492 0.0 18.10 0 0.7130 6.317 83.0 2.7344 24 666 20.2 396.90
## 464 5.82115 0.0 18.10 0 0.7130 6.513 89.9 2.8016 24 666 20.2 393.82
## 465 7.83932 0.0 18.10 0 0.6550 6.209 65.4 2.9634 24 666 20.2 396.90
## 466 3.16360 0.0 18.10 0 0.6550 5.759 48.2 3.0665 24 666 20.2 334.40
## 467 3.77498 0.0 18.10 0 0.6550 5.952 84.7 2.8715 24 666 20.2 22.01
## 468 4.42228 0.0 18.10 0 0.5840 6.003 94.5 2.5403 24 666 20.2 331.29
## 469 15.57570 0.0 18.10 0 0.5800 5.926 71.0 2.9084 24 666 20.2 368.74
## 470 13.07510 0.0 18.10 0 0.5800 5.713 56.7 2.8237 24 666 20.2 396.90
## 471 4.34879 0.0 18.10 0 0.5800 6.167 84.0 3.0334 24 666 20.2 396.90
## 472 4.03841 0.0 18.10 0 0.5320 6.229 90.7 3.0993 24 666 20.2 395.33
## 473 3.56868 0.0 18.10 0 0.5800 6.437 75.0 2.8965 24 666 20.2 393.37
## 474 4.64689 0.0 18.10 0 0.6140 6.980 67.6 2.5329 24 666 20.2 374.68
## 475 8.05579 0.0 18.10 0 0.5840 5.427 95.4 2.4298 24 666 20.2 352.58
## 476 6.39312 0.0 18.10 0 0.5840 6.162 97.4 2.2060 24 666 20.2 302.76
## 477 4.87141 0.0 18.10 0 0.6140 6.484 93.6 2.3053 24 666 20.2 396.21
## 478 15.02340 0.0 18.10 0 0.6140 5.304 97.3 2.1007 24 666 20.2 349.48
## 479 10.23300 0.0 18.10 0 0.6140 6.185 96.7 2.1705 24 666 20.2 379.70
## 480 14.33370 0.0 18.10 0 0.6140 6.229 88.0 1.9512 24 666 20.2 383.32
## 481 5.82401 0.0 18.10 0 0.5320 6.242 64.7 3.4242 24 666 20.2 396.90
## 482 5.70818 0.0 18.10 0 0.5320 6.750 74.9 3.3317 24 666 20.2 393.07
## 483 5.73116 0.0 18.10 0 0.5320 7.061 77.0 3.4106 24 666 20.2 395.28
## 484 2.81838 0.0 18.10 0 0.5320 5.762 40.3 4.0983 24 666 20.2 392.92
## 485 2.37857 0.0 18.10 0 0.5830 5.871 41.9 3.7240 24 666 20.2 370.73
## 486 3.67367 0.0 18.10 0 0.5830 6.312 51.9 3.9917 24 666 20.2 388.62
## 487 5.69175 0.0 18.10 0 0.5830 6.114 79.8 3.5459 24 666 20.2 392.68
## 488 4.83567 0.0 18.10 0 0.5830 5.905 53.2 3.1523 24 666 20.2 388.22
## 489 0.15086 0.0 27.74 0 0.6090 5.454 92.7 1.8209 4 711 20.1 395.09
## 490 0.18337 0.0 27.74 0 0.6090 5.414 98.3 1.7554 4 711 20.1 344.05
## 491 0.20746 0.0 27.74 0 0.6090 5.093 98.0 1.8226 4 711 20.1 318.43
## 492 0.10574 0.0 27.74 0 0.6090 5.983 98.8 1.8681 4 711 20.1 390.11
## 493 0.11132 0.0 27.74 0 0.6090 5.983 83.5 2.1099 4 711 20.1 396.90
## 494 0.17331 0.0 9.69 0 0.5850 5.707 54.0 2.3817 6 391 19.2 396.90
## 495 0.27957 0.0 9.69 0 0.5850 5.926 42.6 2.3817 6 391 19.2 396.90
## 496 0.17899 0.0 9.69 0 0.5850 5.670 28.8 2.7986 6 391 19.2 393.29
## 497 0.28960 0.0 9.69 0 0.5850 5.390 72.9 2.7986 6 391 19.2 396.90
## 498 0.26838 0.0 9.69 0 0.5850 5.794 70.6 2.8927 6 391 19.2 396.90
## 499 0.23912 0.0 9.69 0 0.5850 6.019 65.3 2.4091 6 391 19.2 396.90
## 500 0.17783 0.0 9.69 0 0.5850 5.569 73.5 2.3999 6 391 19.2 395.77
## 501 0.22438 0.0 9.69 0 0.5850 6.027 79.7 2.4982 6 391 19.2 396.90
## 502 0.06263 0.0 11.93 0 0.5730 6.593 69.1 2.4786 1 273 21.0 391.99
## 503 0.04527 0.0 11.93 0 0.5730 6.120 76.7 2.2875 1 273 21.0 396.90
## 504 0.06076 0.0 11.93 0 0.5730 6.976 91.0 2.1675 1 273 21.0 396.90
## 505 0.10959 0.0 11.93 0 0.5730 6.794 89.3 2.3889 1 273 21.0 393.45
## 506 0.04741 0.0 11.93 0 0.5730 6.030 80.8 2.5050 1 273 21.0 396.90
## lstat medv
## 1 4.98 24.0
## 2 9.14 21.6
## 3 4.03 34.7
## 4 2.94 33.4
## 5 5.33 36.2
## 6 5.21 28.7
## 7 12.43 22.9
## 8 19.15 27.1
## 9 29.93 16.5
## 10 17.10 18.9
## 11 20.45 15.0
## 12 13.27 18.9
## 13 15.71 21.7
## 14 8.26 20.4
## 15 10.26 18.2
## 16 8.47 19.9
## 17 6.58 23.1
## 18 14.67 17.5
## 19 11.69 20.2
## 20 11.28 18.2
## 21 21.02 13.6
## 22 13.83 19.6
## 23 18.72 15.2
## 24 19.88 14.5
## 25 16.30 15.6
## 26 16.51 13.9
## 27 14.81 16.6
## 28 17.28 14.8
## 29 12.80 18.4
## 30 11.98 21.0
## 31 22.60 12.7
## 32 13.04 14.5
## 33 27.71 13.2
## 34 18.35 13.1
## 35 20.34 13.5
## 36 9.68 18.9
## 37 11.41 20.0
## 38 8.77 21.0
## 39 10.13 24.7
## 40 4.32 30.8
## 41 1.98 34.9
## 42 4.84 26.6
## 43 5.81 25.3
## 44 7.44 24.7
## 45 9.55 21.2
## 46 10.21 19.3
## 47 14.15 20.0
## 48 18.80 16.6
## 49 30.81 14.4
## 50 16.20 19.4
## 51 13.45 19.7
## 52 9.43 20.5
## 53 5.28 25.0
## 54 8.43 23.4
## 55 14.80 18.9
## 56 4.81 35.4
## 57 5.77 24.7
## 58 3.95 31.6
## 59 6.86 23.3
## 60 9.22 19.6
## 61 13.15 18.7
## 62 14.44 16.0
## 63 6.73 22.2
## 64 9.50 25.0
## 65 8.05 33.0
## 66 4.67 23.5
## 67 10.24 19.4
## 68 8.10 22.0
## 69 13.09 17.4
## 70 8.79 20.9
## 71 6.72 24.2
## 72 9.88 21.7
## 73 5.52 22.8
## 74 7.54 23.4
## 75 6.78 24.1
## 76 8.94 21.4
## 77 11.97 20.0
## 78 10.27 20.8
## 79 12.34 21.2
## 80 9.10 20.3
## 81 5.29 28.0
## 82 7.22 23.9
## 83 6.72 24.8
## 84 7.51 22.9
## 85 9.62 23.9
## 86 6.53 26.6
## 87 12.86 22.5
## 88 8.44 22.2
## 89 5.50 23.6
## 90 5.70 28.7
## 91 8.81 22.6
## 92 8.20 22.0
## 93 8.16 22.9
## 94 6.21 25.0
## 95 10.59 20.6
## 96 6.65 28.4
## 97 11.34 21.4
## 98 4.21 38.7
## 99 3.57 43.8
## 100 6.19 33.2
## 101 9.42 27.5
## 102 7.67 26.5
## 103 10.63 18.6
## 104 13.44 19.3
## 105 12.33 20.1
## 106 16.47 19.5
## 107 18.66 19.5
## 108 14.09 20.4
## 109 12.27 19.8
## 110 15.55 19.4
## 111 13.00 21.7
## 112 10.16 22.8
## 113 16.21 18.8
## 114 17.09 18.7
## 115 10.45 18.5
## 116 15.76 18.3
## 117 12.04 21.2
## 118 10.30 19.2
## 119 15.37 20.4
## 120 13.61 19.3
## 121 14.37 22.0
## 122 14.27 20.3
## 123 17.93 20.5
## 124 25.41 17.3
## 125 17.58 18.8
## 126 14.81 21.4
## 127 27.26 15.7
## 128 17.19 16.2
## 129 15.39 18.0
## 130 18.34 14.3
## 131 12.60 19.2
## 132 12.26 19.6
## 133 11.12 23.0
## 134 15.03 18.4
## 135 17.31 15.6
## 136 16.96 18.1
## 137 16.90 17.4
## 138 14.59 17.1
## 139 21.32 13.3
## 140 18.46 17.8
## 141 24.16 14.0
## 142 34.41 14.4
## 143 26.82 13.4
## 144 26.42 15.6
## 145 29.29 11.8
## 146 27.80 13.8
## 147 16.65 15.6
## 148 29.53 14.6
## 149 28.32 17.8
## 150 21.45 15.4
## 151 14.10 21.5
## 152 13.28 19.6
## 153 12.12 15.3
## 154 15.79 19.4
## 155 15.12 17.0
## 156 15.02 15.6
## 157 16.14 13.1
## 158 4.59 41.3
## 159 6.43 24.3
## 160 7.39 23.3
## 161 5.50 27.0
## 162 1.73 50.0
## 163 1.92 50.0
## 164 3.32 50.0
## 165 11.64 22.7
## 166 9.81 25.0
## 167 3.70 50.0
## 168 12.14 23.8
## 169 11.10 23.8
## 170 11.32 22.3
## 171 14.43 17.4
## 172 12.03 19.1
## 173 14.69 23.1
## 174 9.04 23.6
## 175 9.64 22.6
## 176 5.33 29.4
## 177 10.11 23.2
## 178 6.29 24.6
## 179 6.92 29.9
## 180 5.04 37.2
## 181 7.56 39.8
## 182 9.45 36.2
## 183 4.82 37.9
## 184 5.68 32.5
## 185 13.98 26.4
## 186 13.15 29.6
## 187 4.45 50.0
## 188 6.68 32.0
## 189 4.56 29.8
## 190 5.39 34.9
## 191 5.10 37.0
## 192 4.69 30.5
## 193 2.87 36.4
## 194 5.03 31.1
## 195 4.38 29.1
## 196 2.97 50.0
## 197 4.08 33.3
## 198 8.61 30.3
## 199 6.62 34.6
## 200 4.56 34.9
## 201 4.45 32.9
## 202 7.43 24.1
## 203 3.11 42.3
## 204 3.81 48.5
## 205 2.88 50.0
## 206 10.87 22.6
## 207 10.97 24.4
## 208 18.06 22.5
## 209 14.66 24.4
## 210 23.09 20.0
## 211 17.27 21.7
## 212 23.98 19.3
## 213 16.03 22.4
## 214 9.38 28.1
## 215 29.55 23.7
## 216 9.47 25.0
## 217 13.51 23.3
## 218 9.69 28.7
## 219 17.92 21.5
## 220 10.50 23.0
## 221 9.71 26.7
## 222 21.46 21.7
## 223 9.93 27.5
## 224 7.60 30.1
## 225 4.14 44.8
## 226 4.63 50.0
## 227 3.13 37.6
## 228 6.36 31.6
## 229 3.92 46.7
## 230 3.76 31.5
## 231 11.65 24.3
## 232 5.25 31.7
## 233 2.47 41.7
## 234 3.95 48.3
## 235 8.05 29.0
## 236 10.88 24.0
## 237 9.54 25.1
## 238 4.73 31.5
## 239 6.36 23.7
## 240 7.37 23.3
## 241 11.38 22.0
## 242 12.40 20.1
## 243 11.22 22.2
## 244 5.19 23.7
## 245 12.50 17.6
## 246 18.46 18.5
## 247 9.16 24.3
## 248 10.15 20.5
## 249 9.52 24.5
## 250 6.56 26.2
## 251 5.90 24.4
## 252 3.59 24.8
## 253 3.53 29.6
## 254 3.54 42.8
## 255 6.57 21.9
## 256 9.25 20.9
## 257 3.11 44.0
## 258 5.12 50.0
## 259 7.79 36.0
## 260 6.90 30.1
## 261 9.59 33.8
## 262 7.26 43.1
## 263 5.91 48.8
## 264 11.25 31.0
## 265 8.10 36.5
## 266 10.45 22.8
## 267 14.79 30.7
## 268 7.44 50.0
## 269 3.16 43.5
## 270 13.65 20.7
## 271 13.00 21.1
## 272 6.59 25.2
## 273 7.73 24.4
## 274 6.58 35.2
## 275 3.53 32.4
## 276 2.98 32.0
## 277 6.05 33.2
## 278 4.16 33.1
## 279 7.19 29.1
## 280 4.85 35.1
## 281 3.76 45.4
## 282 4.59 35.4
## 283 3.01 46.0
## 284 3.16 50.0
## 285 7.85 32.2
## 286 8.23 22.0
## 287 12.93 20.1
## 288 7.14 23.2
## 289 7.60 22.3
## 290 9.51 24.8
## 291 3.33 28.5
## 292 3.56 37.3
## 293 4.70 27.9
## 294 8.58 23.9
## 295 10.40 21.7
## 296 6.27 28.6
## 297 7.39 27.1
## 298 15.84 20.3
## 299 4.97 22.5
## 300 4.74 29.0
## 301 6.07 24.8
## 302 9.50 22.0
## 303 8.67 26.4
## 304 4.86 33.1
## 305 6.93 36.1
## 306 8.93 28.4
## 307 6.47 33.4
## 308 7.53 28.2
## 309 4.54 22.8
## 310 9.97 20.3
## 311 12.64 16.1
## 312 5.98 22.1
## 313 11.72 19.4
## 314 7.90 21.6
## 315 9.28 23.8
## 316 11.50 16.2
## 317 18.33 17.8
## 318 15.94 19.8
## 319 10.36 23.1
## 320 12.73 21.0
## 321 7.20 23.8
## 322 6.87 23.1
## 323 7.70 20.4
## 324 11.74 18.5
## 325 6.12 25.0
## 326 5.08 24.6
## 327 6.15 23.0
## 328 12.79 22.2
## 329 9.97 19.3
## 330 7.34 22.6
## 331 9.09 19.8
## 332 12.43 17.1
## 333 7.83 19.4
## 334 5.68 22.2
## 335 6.75 20.7
## 336 8.01 21.1
## 337 9.80 19.5
## 338 10.56 18.5
## 339 8.51 20.6
## 340 9.74 19.0
## 341 9.29 18.7
## 342 5.49 32.7
## 343 8.65 16.5
## 344 7.18 23.9
## 345 4.61 31.2
## 346 10.53 17.5
## 347 12.67 17.2
## 348 6.36 23.1
## 349 5.99 24.5
## 350 5.89 26.6
## 351 5.98 22.9
## 352 5.49 24.1
## 353 7.79 18.6
## 354 4.50 30.1
## 355 8.05 18.2
## 356 5.57 20.6
## 357 17.60 17.8
## 358 13.27 21.7
## 359 11.48 22.7
## 360 12.67 22.6
## 361 7.79 25.0
## 362 14.19 19.9
## 363 10.19 20.8
## 364 14.64 16.8
## 365 5.29 21.9
## 366 7.12 27.5
## 367 14.00 21.9
## 368 13.33 23.1
## 369 3.26 50.0
## 370 3.73 50.0
## 371 2.96 50.0
## 372 9.53 50.0
## 373 8.88 50.0
## 374 34.77 13.8
## 375 37.97 13.8
## 376 13.44 15.0
## 377 23.24 13.9
## 378 21.24 13.3
## 379 23.69 13.1
## 380 21.78 10.2
## 381 17.21 10.4
## 382 21.08 10.9
## 383 23.60 11.3
## 384 24.56 12.3
## 385 30.63 8.8
## 386 30.81 7.2
## 387 28.28 10.5
## 388 31.99 7.4
## 389 30.62 10.2
## 390 20.85 11.5
## 391 17.11 15.1
## 392 18.76 23.2
## 393 25.68 9.7
## 394 15.17 13.8
## 395 16.35 12.7
## 396 17.12 13.1
## 397 19.37 12.5
## 398 19.92 8.5
## 399 30.59 5.0
## 400 29.97 6.3
## 401 26.77 5.6
## 402 20.32 7.2
## 403 20.31 12.1
## 404 19.77 8.3
## 405 27.38 8.5
## 406 22.98 5.0
## 407 23.34 11.9
## 408 12.13 27.9
## 409 26.40 17.2
## 410 19.78 27.5
## 411 10.11 15.0
## 412 21.22 17.2
## 413 34.37 17.9
## 414 20.08 16.3
## 415 36.98 7.0
## 416 29.05 7.2
## 417 25.79 7.5
## 418 26.64 10.4
## 419 20.62 8.8
## 420 22.74 8.4
## 421 15.02 16.7
## 422 15.70 14.2
## 423 14.10 20.8
## 424 23.29 13.4
## 425 17.16 11.7
## 426 24.39 8.3
## 427 15.69 10.2
## 428 14.52 10.9
## 429 21.52 11.0
## 430 24.08 9.5
## 431 17.64 14.5
## 432 19.69 14.1
## 433 12.03 16.1
## 434 16.22 14.3
## 435 15.17 11.7
## 436 23.27 13.4
## 437 18.05 9.6
## 438 26.45 8.7
## 439 34.02 8.4
## 440 22.88 12.8
## 441 22.11 10.5
## 442 19.52 17.1
## 443 16.59 18.4
## 444 18.85 15.4
## 445 23.79 10.8
## 446 23.98 11.8
## 447 17.79 14.9
## 448 16.44 12.6
## 449 18.13 14.1
## 450 19.31 13.0
## 451 17.44 13.4
## 452 17.73 15.2
## 453 17.27 16.1
## 454 16.74 17.8
## 455 18.71 14.9
## 456 18.13 14.1
## 457 19.01 12.7
## 458 16.94 13.5
## 459 16.23 14.9
## 460 14.70 20.0
## 461 16.42 16.4
## 462 14.65 17.7
## 463 13.99 19.5
## 464 10.29 20.2
## 465 13.22 21.4
## 466 14.13 19.9
## 467 17.15 19.0
## 468 21.32 19.1
## 469 18.13 19.1
## 470 14.76 20.1
## 471 16.29 19.9
## 472 12.87 19.6
## 473 14.36 23.2
## 474 11.66 29.8
## 475 18.14 13.8
## 476 24.10 13.3
## 477 18.68 16.7
## 478 24.91 12.0
## 479 18.03 14.6
## 480 13.11 21.4
## 481 10.74 23.0
## 482 7.74 23.7
## 483 7.01 25.0
## 484 10.42 21.8
## 485 13.34 20.6
## 486 10.58 21.2
## 487 14.98 19.1
## 488 11.45 20.6
## 489 18.06 15.2
## 490 23.97 7.0
## 491 29.68 8.1
## 492 18.07 13.6
## 493 13.35 20.1
## 494 12.01 21.8
## 495 13.59 24.5
## 496 17.60 23.1
## 497 21.14 19.7
## 498 14.10 18.3
## 499 12.92 21.2
## 500 15.10 17.5
## 501 14.33 16.8
## 502 9.67 22.4
## 503 9.08 20.6
## 504 5.64 23.9
## 505 6.48 22.0
## 506 7.88 11.9str(Boston)## 'data.frame': 506 obs. of 14 variables:
## $ crim : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
## $ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
## $ indus : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...
## $ chas : int 0 0 0 0 0 0 0 0 0 0 ...
## $ nox : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...
## $ rm : num 6.58 6.42 7.18 7 7.15 ...
## $ age : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
## $ dis : num 4.09 4.97 4.97 6.06 6.06 ...
## $ rad : int 1 2 2 3 3 3 5 5 5 5 ...
## $ tax : num 296 242 242 222 222 222 311 311 311 311 ...
## $ ptratio: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...
## $ black : num 397 397 393 395 397 ...
## $ lstat : num 4.98 9.14 4.03 2.94 5.33 ...
## $ medv : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...Boston-dataframe describes housing values in suburbs of Boston. It has 506 samples and 14 variables. Following info is from the RDocumentation of the MASS-package:
- crim: per capita crime rate by town.
- zn: proportion of residential land zoned for lots over 25,000 sq.ft.
- indus: proportion of non-retail business acres per town.
- chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
- nox: nitrogen oxides concentration (parts per 10 million).
- rm: average number of rooms per dwelling.
- age: proportion of owner-occupied units built prior to 1940.
- dis: weighted mean of distances to five Boston employment centres.
- rad: index of accessibility to radial highways.
- tax: full-value property-tax rate per $10,000.
- ptratio: pupil-teacher ratio by town.
- black: \(1000(Bk - 0.63)^2\) where \(Bk\) is the proportion of blacks by town.
- lstat: lower status of the population (percent).
- medv: median value of owner-occupied homes in $1000s.
The first task is to show graphical overview of the data, summaries of the variables and relationships between variables in the data. This will be done with 3 parts: - Graphical overwies of the variables are shown with following:
#With following code, density graphs are counted (density ()) and plotted to show the distribution of variables
par(mfrow=c(2,4))
for(i in 1:14){
nimi<- colnames(Boston)[i]
data<- density(Boston[,i])
plot(data, main = nimi)
}
From quick observation, we can see that by very inaccurate visual interpretation:
- variables that have peak on the small values:
- crim: per capita crime rate by town.
- zn: proportion of residential land zoned for lots over 25,000 sq.ft.
- chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
- dis: weighted mean of distances to five Boston employment centres.
- variables that have peak on high values:
- age: proportion of owner-occupied units built prior to 1940.
- ptratio: pupil-teacher ratio by town.
- black: \(1000(Bk - 0.63)^2\) where \(Bk\) is the proportion of blacks by town.
- variables that are normally distributed:
- lstat: lower status of the population (percent).
- medv: median value of owner-occupied homes in $1000s.
- nox: nitrogen oxides concentration (parts per 10 million).
- rm: average number of rooms per dwelling.
-variables that have two peaks:
- indus: proportion of non-retail business acres per town.
- rad: index of accessibility to radial highways.
- tax: full-value property-tax rate per $10,000.
Then, lets see the summaries of our data:
for(i in 1:14){
nimi<- colnames(Boston)[i]
data<- summary(Boston[,i])
print(nimi)
print(data)
}## [1] "crim"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00632 0.08204 0.25651 3.61352 3.67708 88.97620
## [1] "zn"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 11.36 12.50 100.00
## [1] "indus"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.46 5.19 9.69 11.14 18.10 27.74
## [1] "chas"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06917 0.00000 1.00000
## [1] "nox"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3850 0.4490 0.5380 0.5547 0.6240 0.8710
## [1] "rm"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.561 5.886 6.208 6.285 6.623 8.780
## [1] "age"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.90 45.02 77.50 68.57 94.08 100.00
## [1] "dis"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.130 2.100 3.207 3.795 5.188 12.127
## [1] "rad"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 9.549 24.000 24.000
## [1] "tax"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 187.0 279.0 330.0 408.2 666.0 711.0
## [1] "ptratio"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.60 17.40 19.05 18.46 20.20 22.00
## [1] "black"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.32 375.38 391.44 356.67 396.23 396.90
## [1] "lstat"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.73 6.95 11.36 12.65 16.95 37.97
## [1] "medv"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 17.02 21.20 22.53 25.00 50.00And finally, correlation plot: The hierarchail clustering is used for ordering variables by using “order =”hclust""
par(mfrow=c(1,2))
#Lets count correlation matrix
correlations<-cor(Boston)
#Lets count significances:
testRes<- cor.mtest(Boston)
#Lets plot it, variables that are blank are unsignificant
corrplot(correlations, p.mat =testRes$p, insig = "blank",method = "number", order = "hclust", addrect = 2, number.cex = 0.75)
From the correlation plot, we can see that (these include only significant correlation values: - variables ptratio, lstat, age, indus, nox, crim, rad and tax seem to correlate positively -variables dis, medv and rm seem to correlate positively with chas, black, and zn and obviously with themselves -chas only correlates positively with rm, medv and dis and negatively with ptratio -The strognest positive correlation is between rad and tax
Dataset scaling
So, the next task is to scale Boston-dataset.
BostonScaled<- as.data.frame(scale(Boston))
#Lets plot summaries of scaled boston dataset and under each column also the uncsaled data so we can see what changed:
for(i in 1:14){
nimi<- colnames(BostonScaled)[i]
data1<- summary(BostonScaled[,i])
data2<-summary(Boston[,i])
print(nimi)
print(data1)
print(data2)
}## [1] "crim"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.419367 -0.410563 -0.390280 0.000000 0.007389 9.924110
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00632 0.08204 0.25651 3.61352 3.67708 88.97620
## [1] "zn"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.48724 -0.48724 -0.48724 0.00000 0.04872 3.80047
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 11.36 12.50 100.00
## [1] "indus"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.5563 -0.8668 -0.2109 0.0000 1.0150 2.4202
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.46 5.19 9.69 11.14 18.10 27.74
## [1] "chas"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.2723 -0.2723 -0.2723 0.0000 -0.2723 3.6648
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06917 0.00000 1.00000
## [1] "nox"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.4644 -0.9121 -0.1441 0.0000 0.5981 2.7296
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3850 0.4490 0.5380 0.5547 0.6240 0.8710
## [1] "rm"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.8764 -0.5681 -0.1084 0.0000 0.4823 3.5515
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.561 5.886 6.208 6.285 6.623 8.780
## [1] "age"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.3331 -0.8366 0.3171 0.0000 0.9059 1.1164
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.90 45.02 77.50 68.57 94.08 100.00
## [1] "dis"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.2658 -0.8049 -0.2790 0.0000 0.6617 3.9566
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.130 2.100 3.207 3.795 5.188 12.127
## [1] "rad"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.9819 -0.6373 -0.5225 0.0000 1.6596 1.6596
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 9.549 24.000 24.000
## [1] "tax"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.3127 -0.7668 -0.4642 0.0000 1.5294 1.7964
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 187.0 279.0 330.0 408.2 666.0 711.0
## [1] "ptratio"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.7047 -0.4876 0.2746 0.0000 0.8058 1.6372
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.60 17.40 19.05 18.46 20.20 22.00
## [1] "black"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.9033 0.2049 0.3808 0.0000 0.4332 0.4406
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.32 375.38 391.44 356.67 396.23 396.90
## [1] "lstat"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.5296 -0.7986 -0.1811 0.0000 0.6024 3.5453
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.73 6.95 11.36 12.65 16.95 37.97
## [1] "medv"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.9063 -0.5989 -0.1449 0.0000 0.2683 2.9865
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 17.02 21.20 22.53 25.00 50.00Okay, from values above, we can see that scale-function scaled everything to have mean as 0 and also every value now represents how many standard devitions it is from the mean. These scaled values are actually called as z-score.
Then, the next task is to divide variable crime-rate into quantiles and create a categorical variable called crime:
#Lets count quantiles
bins<- quantile(BostonScaled$crim)
#Lets set neq variable with this ifelse-script.
#We start with smallest and in no-option we include the test to categorize it for further
BostonScaled$crime<- ifelse(BostonScaled$crim<bins[2], paste("[", round(bins[1], digits = 3), ",", round(bins[2], digits = 3), "]", sep = ""),
ifelse(BostonScaled$crim<bins[3], paste("(", round(bins[2], digits = 3), ",", round(bins[3], digits = 3), "]", sep = ""),
ifelse(BostonScaled$crim<bins[4], paste("(", round(bins[3], digits = 3), ",", round(bins[4], digits = 3), "]", sep = ""), paste("(",round(bins[4], digits = 3), ",", round(bins[5], digits = 3), "]", sep = "") )))
table(BostonScaled$crime)##
## (-0.39,0.007] (-0.411,-0.39] (0.007,9.924] [-0.419,-0.411]
## 126 126 127 127BostonScaled<- BostonScaled[,2:15]
#In datacamp these were changed into following classes: low, med_low, med_high, high, so, just for clarification, we shall do the same
unique(BostonScaled$crime)## [1] "[-0.419,-0.411]" "(-0.411,-0.39]" "(-0.39,0.007]" "(0.007,9.924]"BostonScaled$crime<- ifelse(BostonScaled$crime=="[-0.419,-0.411]", "low", BostonScaled$crime)
BostonScaled$crime<- ifelse(BostonScaled$crime=="(-0.411,-0.39]", "low_med", BostonScaled$crime)
BostonScaled$crime<- ifelse(BostonScaled$crime=="(-0.39,0.007]", "high_med", BostonScaled$crime)
BostonScaled$crime<- ifelse(BostonScaled$crime=="(0.007,9.924]", "high", BostonScaled$crime)
table(BostonScaled$crime)##
## high high_med low low_med
## 127 126 127 126All good! Okay, now we have created the variable crime, Then, lets divide the data into train and test sets (80% into train, 20% into test:
#Lets create our tran-data subset
trainData<- BostonScaled[(sample(nrow(BostonScaled), size= 0.8*nrow(BostonScaled))),]
#And the leftovers are testdata
testData<- subset(BostonScaled, is.element(rownames(BostonScaled), rownames(trainData))==F)Lets fit linear discriminant analysis on the train set
So, the next task to do is to fit linear discriminant analysis into he train dataset.
#LDA_analysis
ldadata<- lda(crime ~ ., data = trainData)
#Lets create numeric crime vector maintaining the original arrangement of values
trainData$crime<- ifelse(trainData$crime=="low", 1, ifelse(trainData$crime=="low_med", 2, ifelse(trainData$crime=="high_med", 3, 4)))
#Then the arrows-function from datacamp
lda.arrows <- function(x, myscale = 1, arrow_heads = 0.1, color = "red", tex = 0.75, choices = c(1,2)){
heads <- coef(x)
arrows(x0 = 0, y0 = 0,
x1 = myscale * heads[,choices[1]],
y1 = myscale * heads[,choices[2]], col=color, length = arrow_heads)
text(myscale * heads[,choices], labels = row.names(heads),
cex = tex, col=color, pos=3)
}
#Lets define classes-variable
classes <- as.numeric(trainData$crime)
#lets create biplot and also the arrows, classes defines colors and also symbols for variables
plot(ldadata, dimen = 2, col = classes, pch = classes)
lda.arrows(ldadata, myscale = 1)
Prediction
First, lets save correct classes of crime-variable in test set:
correct <- testData$crime
testData<- select(testData, -crime)Then, lets do prediction of correct classes in the test data:
ldaprediction<- predict(ldadata, newdata = testData)
table(correct= correct, predicted = ldaprediction$class)## predicted
## correct high high_med low low_med
## high 26 0 0 0
## high_med 3 14 0 12
## low 0 2 16 8
## low_med 0 3 3 15So, for some reason, my predictive function does not predict correctly classes low_med and low, some low values are predicted to be low_med. However, with every other type of variable, predicions are quite accurate:
- 100% high values are predicted correctly
- 64% of high_med values are in correct category
- 57% of low_med values are in correct gategory - BUT only 29% of low values are in low category
Distances
So, lets do bostonScaled2, that is the original Boston dataset scaled
BostonScaled2<-scale(Boston) Then, lets count euclidean and manhattan distances of the dataset:
eu<- dist(BostonScaled2, method = "euclidean")#This is the hypothenuse distance, so absolutely the shortest distance
man<- dist(BostonScaled2, method = "manhattan")#This is the sum of individual distances between variables (like in manhattan when you have to navigate between square-shaped houses)Now, lets do k-means clustering:
#first, lets set seed
set.seed(56)
#Then, lets count the WCSS (within cluster sum of squares) and define the optimal number of clusters (when this drops drastically)
#We set the maximal number of clusters to be 10, just for the sake of time and computer
#we do kmeans from k==1 to k ==10, and we have tot.whitinss as a outcome variable that is saved into WCSS
WCSS<- sapply(1:10, function(k){kmeans(BostonScaled2, k)$tot.withinss})
qplot(x=1:10, y=WCSS, geom="line")
#From here we can see that the greatest drop (so biggest variation is somewhere around 2, so optimal number of clusters is 2)
BostonScaled2<- as.data.frame(BostonScaled2)
km<-kmeans(BostonScaled2, centers = 2)
BostonScaled2$cluster<- km$cluster
BostonScaled2$cluster<- as.factor(BostonScaled2$cluster)
ggpairs(BostonScaled2, upper = "blank", diag = "blank", mapping=ggplot2::aes(colour = cluster))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Interpretation of the data: Even though our data is in very tiny images, we can see the distribution of colors: Two clusters separate the variable crim beautifully. Also zn is beautifully divided. Same is true for indus, dis and nox variables. However, I don’t think other variables are separating by these two clusters.
Bonus 1
- Lets scale the Boston-dataset to BostonScaled3. Firstly, Lets select k as 5 and perform k-means clustering,
BostonScaled3<- scale(Boston)
km<-kmeans(BostonScaled2, centers = 5)
BostonScaled3<- as.data.frame(BostonScaled3)
BostonScaled3$cluster<- km$cluster
#then, lets perform LDA wth clusters as target classes
LDA<- ldadata<- lda(cluster ~ ., data = BostonScaled3)
#Lets define the function Lda-arrows again.
lda.arrows <- function(x, myscale = 1, arrow_heads = 0.1, color = "red", tex = 0.75, choices = c(1,2)){
heads <- coef(x)
arrows(x0 = 0, y0 = 0,
x1 = myscale * heads[,choices[1]],
y1 = myscale * heads[,choices[2]], col=color, length = arrow_heads)
text(myscale * heads[,choices], labels = row.names(heads),
cex = tex, col=color, pos=3)
}
#Lets define classes-variable
classes <- as.numeric(BostonScaled3$cluster)
#lets create biplot and also the arrows, classes defines colors and also symbols for variables
plot(LDA, dimen = 2, col = classes, pch = classes)
lda.arrows(LDA, myscale = 3)
So, lets interprate results. nec, induce. rad are great separators of clusters. Age and crim, also zn are separating clusters 1 and 2 from 4,3 and 5. I would say that age is the strongest linear separator, because it has longest vector.
Super-Bonus
First, lets run the code:
model_predictors <- select(trainData, -crime)
# check the dimensions
dim(model_predictors)## [1] 404 13#For some reasons the crime was till included, while it was not in trainData, so we skip the row 1
ldadata$scaling<- ldadata$scaling[2:14,]
dim(ldadata$scaling)## [1] 13 4# matrix multiplication
matrix_product <- as.matrix(model_predictors) %*% ldadata$scaling
matrix_product <- as.data.frame(matrix_product)
#Lets adjust the color
par(mfrow= c(2,2))
plot_ly(x = matrix_product$LD1, y = matrix_product$LD2, z = matrix_product$LD3, type= 'scatter3d', mode='markers', color = trainData$crime)km<- kmeans(trainData, centers = 5)
#Then, lets define the color by cluster
trainData$clusters<- km$cluster
plot_ly(x = matrix_product$LD1, y = matrix_product$LD2, z = matrix_product$LD3, type= 'scatter3d', mode='markers', color = trainData$clusters)These are week 4 excercises.
#Libraries used for this code
library(GGally)
library("factoextra")## Warning: package 'factoextra' was built under R version 3.6.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(readr)
library("FactoMineR")## Warning: package 'FactoMineR' was built under R version 3.6.3library("tidyverse")## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.1 v stringr 1.4.0
## v tidyr 1.1.3 v forcats 0.5.1
## v purrr 0.3.4## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'tidyr' was built under R version 3.6.3## Warning: package 'purrr' was built under R version 3.6.3## Warning: package 'stringr' was built under R version 3.6.2## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x gridExtra::combine() masks dplyr::combine()
## x plotly::filter() masks dplyr::filter(), stats::filter()
## x dplyr::lag() masks stats::lag()
## x plotly::select() masks MASS::select(), dplyr::select()
## x Hmisc::src() masks dplyr::src()
## x Hmisc::summarize() masks dplyr::summarize()human4 <- read_csv("human.csv")## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_character(),
## Edu2.FM = col_double(),
## Labo.FM = col_double(),
## Edu.Exp = col_double(),
## Life.Exp = col_double(),
## GNI = col_double(),
## Mat.Mor = col_double(),
## Ado.Birth = col_double(),
## Parli.F = col_double()
## )human4<- as.data.frame(human4)
colnames(human4)[1]<- "Country"In these excercises we will be using the human-data that is from UN website. Our data has 8 columns and 155 countries as rows. Variables as columns are following:
-“Edu2.FM” = population qith at least some secondary education.
-“Labo.FM” = labor force participation rate (15years and older).
-“Edu.Exp” = expected education
-“Life.Exp” = Life expectancy
-“GNI” = Gender inequality index
-“Mat.Mor” = maternal mortality ratio
-“Ado.Birth” =Adolescent birt rate
-“Parli.F” =Share of sheats in parlament held by woman.
Lets explore the distributions and relationships of the variables
#First, lets plot the graphical overview.
par(mfrow=c(2,4))
for(i in 2:8){
human4[,i]<- as.numeric(as.character(human4[,i]))
nimi<- colnames(human4)[i]
data<- density(human4[,i])
plot(data, main = nimi)
}
Fom the general density maps of our variables, we can see that: - Labo.FM, Edu.exp, Life.exp,and Parli.F are normally distributed. - GNI, mat.mor and Ado.birth are having more values on the left, so smaller values. - Edu2.FM has two peaks.
Then, lets see the summaries:
summary(human4)## Country Edu2.FM Labo.FM Edu.Exp
## Length:155 Min. :0.1717 Min. :0.1857 Min. : 5.40
## Class :character 1st Qu.:0.7264 1st Qu.:0.5984 1st Qu.:11.25
## Mode :character Median :0.9375 Median :0.7535 Median :13.50
## Mean :0.8529 Mean :0.7074 Mean :13.18
## 3rd Qu.:0.9968 3rd Qu.:0.8535 3rd Qu.:15.20
## Max. :1.4967 Max. :1.0380 Max. :20.20
## Life.Exp GNI Mat.Mor Ado.Birth
## Min. :49.00 Min. : 1.123 Min. : 1.0 Min. : 0.60
## 1st Qu.:66.30 1st Qu.: 5.275 1st Qu.: 11.5 1st Qu.: 12.65
## Median :74.20 Median : 13.054 Median : 49.0 Median : 33.60
## Mean :71.65 Mean : 46.496 Mean : 149.1 Mean : 47.16
## 3rd Qu.:77.25 3rd Qu.: 27.256 3rd Qu.: 190.0 3rd Qu.: 71.95
## Max. :83.50 Max. :908.000 Max. :1100.0 Max. :204.80
## Parli.F
## Min. : 0.00
## 1st Qu.:12.40
## Median :19.30
## Mean :20.91
## 3rd Qu.:27.95
## Max. :57.50Then, lets look at the relationships between variables:
human5<- human4[,2:9]
ggpairs(human5, lower = "blank")+
theme(strip.text.y = element_text(size=6))
Here we can see, that:
- Female secondary education correlates with education expected, life expectancy, - GNI, - maternal mortality and - adolescent birt rate. (- means negatively)
- Females in labor force correlates strongly with: Female share of parlament places and maternal mortality.
- Expected education correlates strongly with: Female share of parlament places, -adolescent birt rate, -maternal mortality, -GNI and life expectancy.
- Life expectancy correlates with Female share of parlament places,-adolescent birt rate, -maternal mortality and -GNI.
- GNI correlates with adolescent birt rate and maternal mortality.
Excercise 2, the PCA
pca<- prcomp(human5)
par(mfrow = c(1, 2))
fviz_pca_ind(pca, geom= c("point", "text"), repel = T, addEllipses = F)
fviz_pca_var(pca)
I did not do a biplot, because with 155 observations it just looks like a mess. However, here we can see. that PCA1 represents 76.9% of the variation, while our dimension 2 has 21.8% of the variation. The greatest sources of variatiob are maternal mortality and GNi, also adolescent birth rate is creating some variation.
Lets do the same for scaled variables
human6<- scale(human5)
pca<- prcomp(human6)
par(mfrow = c(1, 2))
fviz_pca_ind(pca, geom= c("point", "text"), repel = T, addEllipses = F, col.ind = "cos2", gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))+
ggtitle("scaled pca-plot diverging countries by variables behind the GNI-value")
fviz_pca_var(pca)+
ggtitle("scaled pca-plot spesifying variables behind the GNI-value")
fviz_pca_biplot(pca)
Well, now the results differ quite a lot. Here we can see, that in pca1: expected education, amount of women with secondary education, life expectancy and on the other side GNI, maternal mortality and adolescent birt rate are making the differnece, In the second principal component, amount of women in parlament and amount of women in labor force are crating the greatest difference. These results differ more (there are more variables making a significant difference, because variation of the variables is scaled and thus more equal). The range of the values in human 4 (unscaled data frame) is greatest with maternal mortaluty, gni and adolescent birt rate. By the way, the colors represent how well each point is separated (more red, further)
summary(human5)## Edu2.FM Labo.FM Edu.Exp Life.Exp
## Min. :0.1717 Min. :0.1857 Min. : 5.40 Min. :49.00
## 1st Qu.:0.7264 1st Qu.:0.5984 1st Qu.:11.25 1st Qu.:66.30
## Median :0.9375 Median :0.7535 Median :13.50 Median :74.20
## Mean :0.8529 Mean :0.7074 Mean :13.18 Mean :71.65
## 3rd Qu.:0.9968 3rd Qu.:0.8535 3rd Qu.:15.20 3rd Qu.:77.25
## Max. :1.4967 Max. :1.0380 Max. :20.20 Max. :83.50
## GNI Mat.Mor Ado.Birth Parli.F
## Min. : 1.123 Min. : 1.0 Min. : 0.60 Min. : 0.00
## 1st Qu.: 5.275 1st Qu.: 11.5 1st Qu.: 12.65 1st Qu.:12.40
## Median : 13.054 Median : 49.0 Median : 33.60 Median :19.30
## Mean : 46.496 Mean : 149.1 Mean : 47.16 Mean :20.91
## 3rd Qu.: 27.256 3rd Qu.: 190.0 3rd Qu.: 71.95 3rd Qu.:27.95
## Max. :908.000 Max. :1100.0 Max. :204.80 Max. :57.50summary(human6)## Edu2.FM Labo.FM Edu.Exp Life.Exp
## Min. :-2.8189 Min. :-2.6247 Min. :-2.7378 Min. :-2.7188
## 1st Qu.:-0.5233 1st Qu.:-0.5484 1st Qu.:-0.6782 1st Qu.:-0.6425
## Median : 0.3503 Median : 0.2316 Median : 0.1140 Median : 0.3056
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.5958 3rd Qu.: 0.7350 3rd Qu.: 0.7126 3rd Qu.: 0.6717
## Max. : 2.6646 Max. : 1.6632 Max. : 2.4730 Max. : 1.4218
## GNI Mat.Mor Ado.Birth Parli.F
## Min. :-0.3162 Min. :-0.6992 Min. :-1.1325 Min. :-1.8203
## 1st Qu.:-0.2873 1st Qu.:-0.6496 1st Qu.:-0.8394 1st Qu.:-0.7409
## Median :-0.2330 Median :-0.4726 Median :-0.3298 Median :-0.1403
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.1341 3rd Qu.: 0.1932 3rd Qu.: 0.6030 3rd Qu.: 0.6127
## Max. : 6.0037 Max. : 4.4899 Max. : 3.8344 Max. : 3.1850Give your personal interpretations of the first two principal component dimensions based on the biplot drawn after PCA on the standardized human data. (0-2 points)
Lets observe the variations inside each principal component. First, lets see how pca’s are divided.
fviz_eig(pca)
From the graph above, we can observe that 49.8% of the variation is explained with PCa1, and 16.4% with pca2.
res.var <- get_pca_var(pca)
heatmap(res.var$contrib )
From the heatmap below, we can see that for the pca1, variables maternal mortality, life expectancy, education expectancy and adolescent birth are contributing quite much for the total variabce. For the second principal component, we can see that labor.FM and parli.F are contributing quite much, while the rest of the variables are contributing less, This heatmap also gives us some interesting thoughts, because we can see that after certain point, our pca-components start to be very similar to each other, or just separation based on one variable.
Load the tea dataset from the package Factominer. Explore the data briefly: look at the structure and the dimensions of the data and visualize it. Then do Multiple Correspondence Analysis on the tea data (or to a certain columns of the data, it’s up to you). Interpret the results of the MCA and draw at least the variable biplot of the analysis. You can also explore other plotting options for MCA. Comment on the output of the plots
Lets load the dataset and observe the dimensions, structures etc.
data(tea)
str(tea)## 'data.frame': 300 obs. of 36 variables:
## $ breakfast : Factor w/ 2 levels "breakfast","Not.breakfast": 1 1 2 2 1 2 1 2 1 1 ...
## $ tea.time : Factor w/ 2 levels "Not.tea time",..: 1 1 2 1 1 1 2 2 2 1 ...
## $ evening : Factor w/ 2 levels "evening","Not.evening": 2 2 1 2 1 2 2 1 2 1 ...
## $ lunch : Factor w/ 2 levels "lunch","Not.lunch": 2 2 2 2 2 2 2 2 2 2 ...
## $ dinner : Factor w/ 2 levels "dinner","Not.dinner": 2 2 1 1 2 1 2 2 2 2 ...
## $ always : Factor w/ 2 levels "always","Not.always": 2 2 2 2 1 2 2 2 2 2 ...
## $ home : Factor w/ 2 levels "home","Not.home": 1 1 1 1 1 1 1 1 1 1 ...
## $ work : Factor w/ 2 levels "Not.work","work": 1 1 2 1 1 1 1 1 1 1 ...
## $ tearoom : Factor w/ 2 levels "Not.tearoom",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ friends : Factor w/ 2 levels "friends","Not.friends": 2 2 1 2 2 2 1 2 2 2 ...
## $ resto : Factor w/ 2 levels "Not.resto","resto": 1 1 2 1 1 1 1 1 1 1 ...
## $ pub : Factor w/ 2 levels "Not.pub","pub": 1 1 1 1 1 1 1 1 1 1 ...
## $ Tea : Factor w/ 3 levels "black","Earl Grey",..: 1 1 2 2 2 2 2 1 2 1 ...
## $ How : Factor w/ 4 levels "alone","lemon",..: 1 3 1 1 1 1 1 3 3 1 ...
## $ sugar : Factor w/ 2 levels "No.sugar","sugar": 2 1 1 2 1 1 1 1 1 1 ...
## $ how : Factor w/ 3 levels "tea bag","tea bag+unpackaged",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ where : Factor w/ 3 levels "chain store",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ price : Factor w/ 6 levels "p_branded","p_cheap",..: 4 6 6 6 6 3 6 6 5 5 ...
## $ age : int 39 45 47 23 48 21 37 36 40 37 ...
## $ sex : Factor w/ 2 levels "F","M": 2 1 1 2 2 2 2 1 2 2 ...
## $ SPC : Factor w/ 7 levels "employee","middle",..: 2 2 4 6 1 6 5 2 5 5 ...
## $ Sport : Factor w/ 2 levels "Not.sportsman",..: 2 2 2 1 2 2 2 2 2 1 ...
## $ age_Q : Factor w/ 5 levels "15-24","25-34",..: 3 4 4 1 4 1 3 3 3 3 ...
## $ frequency : Factor w/ 4 levels "1/day","1 to 2/week",..: 1 1 3 1 3 1 4 2 3 3 ...
## $ escape.exoticism: Factor w/ 2 levels "escape-exoticism",..: 2 1 2 1 1 2 2 2 2 2 ...
## $ spirituality : Factor w/ 2 levels "Not.spirituality",..: 1 1 1 2 2 1 1 1 1 1 ...
## $ healthy : Factor w/ 2 levels "healthy","Not.healthy": 1 1 1 1 2 1 1 1 2 1 ...
## $ diuretic : Factor w/ 2 levels "diuretic","Not.diuretic": 2 1 1 2 1 2 2 2 2 1 ...
## $ friendliness : Factor w/ 2 levels "friendliness",..: 2 2 1 2 1 2 2 1 2 1 ...
## $ iron.absorption : Factor w/ 2 levels "iron absorption",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ feminine : Factor w/ 2 levels "feminine","Not.feminine": 2 2 2 2 2 2 2 1 2 2 ...
## $ sophisticated : Factor w/ 2 levels "Not.sophisticated",..: 1 1 1 2 1 1 1 2 2 1 ...
## $ slimming : Factor w/ 2 levels "No.slimming",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ exciting : Factor w/ 2 levels "exciting","No.exciting": 2 1 2 2 2 2 2 2 2 2 ...
## $ relaxing : Factor w/ 2 levels "No.relaxing",..: 1 1 2 2 2 2 2 2 2 2 ...
## $ effect.on.health: Factor w/ 2 levels "effect on health",..: 2 2 2 2 2 2 2 2 2 2 ...#The data has 300 obs. of 36 variables.
par(mfrow=c(6,6))
for(i in 1:36){
nimi<- colnames(tea)[i]
data<- as.data.frame(tea[,i])
colnames(data)<- "column"
data$nimi<-nimi
if(i==1){
data2<-data
}else{
data2<- rbind.data.frame(data2, data)
}
}## Warning in `[<-.factor`(`*tmp*`, ri, value = c(39L, 45L, 47L, 23L, 48L, :
## invalid factor level, NA generated ggplot(data2, aes(column, fill=column))+
geom_bar()+
theme_light()+
facet_wrap(vars(nimi), scales= "free", shrink = T)+
theme(legend.position = "none", axis.text.x = element_text(size=5))
So, our data has 36 variables, 35 of them are categorical while age is numeric. SPC and age-Q have most variables, but other classes have only two.
Then, lets look structure and dimensions of the data:
str(tea)## 'data.frame': 300 obs. of 36 variables:
## $ breakfast : Factor w/ 2 levels "breakfast","Not.breakfast": 1 1 2 2 1 2 1 2 1 1 ...
## $ tea.time : Factor w/ 2 levels "Not.tea time",..: 1 1 2 1 1 1 2 2 2 1 ...
## $ evening : Factor w/ 2 levels "evening","Not.evening": 2 2 1 2 1 2 2 1 2 1 ...
## $ lunch : Factor w/ 2 levels "lunch","Not.lunch": 2 2 2 2 2 2 2 2 2 2 ...
## $ dinner : Factor w/ 2 levels "dinner","Not.dinner": 2 2 1 1 2 1 2 2 2 2 ...
## $ always : Factor w/ 2 levels "always","Not.always": 2 2 2 2 1 2 2 2 2 2 ...
## $ home : Factor w/ 2 levels "home","Not.home": 1 1 1 1 1 1 1 1 1 1 ...
## $ work : Factor w/ 2 levels "Not.work","work": 1 1 2 1 1 1 1 1 1 1 ...
## $ tearoom : Factor w/ 2 levels "Not.tearoom",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ friends : Factor w/ 2 levels "friends","Not.friends": 2 2 1 2 2 2 1 2 2 2 ...
## $ resto : Factor w/ 2 levels "Not.resto","resto": 1 1 2 1 1 1 1 1 1 1 ...
## $ pub : Factor w/ 2 levels "Not.pub","pub": 1 1 1 1 1 1 1 1 1 1 ...
## $ Tea : Factor w/ 3 levels "black","Earl Grey",..: 1 1 2 2 2 2 2 1 2 1 ...
## $ How : Factor w/ 4 levels "alone","lemon",..: 1 3 1 1 1 1 1 3 3 1 ...
## $ sugar : Factor w/ 2 levels "No.sugar","sugar": 2 1 1 2 1 1 1 1 1 1 ...
## $ how : Factor w/ 3 levels "tea bag","tea bag+unpackaged",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ where : Factor w/ 3 levels "chain store",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ price : Factor w/ 6 levels "p_branded","p_cheap",..: 4 6 6 6 6 3 6 6 5 5 ...
## $ age : int 39 45 47 23 48 21 37 36 40 37 ...
## $ sex : Factor w/ 2 levels "F","M": 2 1 1 2 2 2 2 1 2 2 ...
## $ SPC : Factor w/ 7 levels "employee","middle",..: 2 2 4 6 1 6 5 2 5 5 ...
## $ Sport : Factor w/ 2 levels "Not.sportsman",..: 2 2 2 1 2 2 2 2 2 1 ...
## $ age_Q : Factor w/ 5 levels "15-24","25-34",..: 3 4 4 1 4 1 3 3 3 3 ...
## $ frequency : Factor w/ 4 levels "1/day","1 to 2/week",..: 1 1 3 1 3 1 4 2 3 3 ...
## $ escape.exoticism: Factor w/ 2 levels "escape-exoticism",..: 2 1 2 1 1 2 2 2 2 2 ...
## $ spirituality : Factor w/ 2 levels "Not.spirituality",..: 1 1 1 2 2 1 1 1 1 1 ...
## $ healthy : Factor w/ 2 levels "healthy","Not.healthy": 1 1 1 1 2 1 1 1 2 1 ...
## $ diuretic : Factor w/ 2 levels "diuretic","Not.diuretic": 2 1 1 2 1 2 2 2 2 1 ...
## $ friendliness : Factor w/ 2 levels "friendliness",..: 2 2 1 2 1 2 2 1 2 1 ...
## $ iron.absorption : Factor w/ 2 levels "iron absorption",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ feminine : Factor w/ 2 levels "feminine","Not.feminine": 2 2 2 2 2 2 2 1 2 2 ...
## $ sophisticated : Factor w/ 2 levels "Not.sophisticated",..: 1 1 1 2 1 1 1 2 2 1 ...
## $ slimming : Factor w/ 2 levels "No.slimming",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ exciting : Factor w/ 2 levels "exciting","No.exciting": 2 1 2 2 2 2 2 2 2 2 ...
## $ relaxing : Factor w/ 2 levels "No.relaxing",..: 1 1 2 2 2 2 2 2 2 2 ...
## $ effect.on.health: Factor w/ 2 levels "effect on health",..: 2 2 2 2 2 2 2 2 2 2 ...dim(tea)## [1] 300 36Then, lets do Multiple Correspondence Analysis for all of our variables.
#Lets first transform all variables to factors, without this we will only have some errors
tea2 <- tea %>% mutate_all(as.factor)
#Then, lets do MCA
Multi<- MCA(tea2, graph = T)
From the MCA-plot, we can conclude following: Age, age-q (catecorigal age) and SPC are the variables that are responsible for the most of the variation along x-axis. They were also the variables with most categories, I wonder if this is because of that.
Sex, friends, where, tea and pub are, also in addition to the variables already mentioned leading to that point.